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Odysseys of agriculture sensors: Current challenges and forthcoming prospects
Abstract
Recent decades have witnessed unprecedented development in the field of biomedical and chemical science due to an economical, precise, and sensitive microelectromechanical system (MEMS) technology. Nevertheless, very few reports have highlighted the importance of MEMS-based sensors in the field of agriculture science and technology. Precision agriculture (PA) is a management strategy that employs advanced sensors marriage with information technology to improve productivity and quality of modern agriculture. This review unfolds the journey the conventional sensors have taken to come to the contemporary MEMS-based sensors. This review explains the fundamental principle of various sensors, presents outlines with a comparative study of sensors engaged in the field of agriculture. We have also elaborated on the importance of microcontroller addition in MEMS sensors to improve their sensitivity and productivity. Besides highlighting the pros and cons of the sensors, this review also brings a crisp discussion on the very recent sensors engaged to benefit agriculture and also takes into account the developmental aspects for commercialization.
... In addition, industrial revolution was an add on to agriculture for enhanced human survival and sound lifestyle [3]. Industrial revolution, though supported a significant rise in agricultural productivity, yet technology support assets are expected to meet the demand [4]. As per United Nation's food and agricultural organization human's population would reach 9.2 billion by the year 2050 and the agricultural produce is to be ascended to 70% to meet the needs [5]. ...
... representing C w -volumetric heat capacity (m −3°C−1 ), C bw -bulk volumetric heat capacity (m −3°C−1 ), V hp as heat pulse velocity (ms −1 ) and J-water flux density [4]. ...
... Schematic of accelerometer working illustration of a capacitive, b piezoelectric[4] ...
Agricultural practices are indigenous, accounts for considerable GDP of India, livelihood for most of the rural population either directly or indirectly and the products obtained are major energy sources of human body. In recent decades due to enormous population growth and rock-bottomed cultivable land, add on to cultivation practices are postulated, adhering to supply apropos to demand of food grains. The extensive research and development in various fields especially in sensory systems in specific Micro Electro Mechanical System (MEMS)-based sensors and automation have witnessed tremendous achievements beyond imagination due to precise sensory technology on tap economically. Withal, the commercially available technology integrated with sensory/automation systems and the research highlights in agriculture reported till date are lowest in equate to rest. Incorporating sensors with electronics and information technology, cited as precision agriculture (PA), meliorates the quality and inflated agricultural yeild. This paper throws a light on the key role of microcontrollers, assorted sensors along with fundamentals of their working, showcasing the applications of sensors for soil parameters of agriculture field for improved productivity and sensitivity. Nevertheless discussing the pros and cons of the ssensors, the paper unfolds the sensors employed for finer agricultural ontogenesis and caters a path for developmental aspects of commercialization.KeywordsMEMSAutomationPrecision agricultureSoil parameters
... By the name of VF automation system using different types of sensors, it can automate most of VF processes, such as we can control watering system or flow control, the moisture level of soil, crops health [68]. However, existing agriculture technology faces challenges in growing crops due to various issues, such as unstable environmental situation, limitation of more food, safety, reliability, cost, life cycle, and overall management [69][70][71]. Therefore, advanced USVF technology sensors generally used to reduce human effort, easy to handle, more effective and more efficient. ...
... The proximity sensors utilize to monitor crop flow during collection data in VF [94]. In [71], the authors usually indicated crop plant temperature control from a heat source measured by a proximity sensor in SF. The study in [94,103] reported proximity sensors are utilized to measure grain yield in the agriculture farm, which helps to reduce human effort, measure accurate crop, save time, and can work in harsh environmental conditions. ...
... VF technology plays an essential role in reducing future food demand, which is one of the world's biggest problems today. However, existing VF technology faces few challenges due to various issues, such as high starting cost, low yield variability, energy reliance and utilization, producing heat by artificial lighting was presented in [41,71,73,74]. In [73,74], the authors reported VF technology would be gradually becoming a non-profit industry if not focus on addressing the current issues such as energy use, pollution, economy. ...
The global economy is now under threat due to the ongoing domestic and international lockdown for COVID-19. Many have already lost their jobs, and businesses have been unstable in the Corona era. Apart from educational institutions, banks, privately owned institutions, and agriculture, there are signs of economic recession in almost all sectors. The roles of modern technology, the Internet of things, and artificial intelligence are undeniable in helping the world achieve economic prosperity in the post-COVID-19 economic downturn. Food production must increase by 60% by 2050 to meet global food security demands in the face of uncertainty such as the COVID-19 pandemic and a growing population. Given COVID 19’s intensity and isolation, improving food production and distribution systems is critical to combating hunger and addressing the double burden of malnutrition. As the world’s population is growing day by day, according to an estimation world’s population reaches 9.6 billion by 2050, so there is a growing need to modify the agriculture methods, technologies so that maximum crops can be attained and human effort can be reduced. The urban smart vertical farming (USVF) is a solution to secure food production, which can be introduced at any adaptive reuse, retrofit, or new buildings in vertical manners. This paper aims to provide a comprehensive review of the concept of USVF using various techniques to enhance productivity as well as its types, topologies, technologies, control systems, social acceptance, and benefits. This review has focused on numerous issues, challenges, and recommendations in the development of the system, vertical farming management, and modern technologies approach.
... La radiación solar es la fuente de calor y energía emitida por el sol que determina la dinámica en la superficie de la tierra (procesos atmosféricos, clima, cultivos) (Singh & Singh, 2020). La radiación solar consiste en ondas electromagnéticas con una cantidad específica de energía por longitud, principalmente de la banda ultravioleta (UV), visible e infrarroja (IR) (Wondraczek et al., 2015). ...
... Los sensores de humedad relativa han sido utilizados ampliamente en el campo de la agricultura, agroindustria, medicina, y metrología (Farahani et al., 2014;Liang et al., 2018). Estos sensores se caracterizan principalmente por su estructura de diseño y sistema integrado, encontrando en el mercado diferentes referencias como resistivos, capacitivos, higrómetros, ópticos y gravimétricos ver Tabla 4. (Ahmad et al., 2017;Singh & Singh., 2020). La temperatura atmosférica ha sido considerada como la variable más importante para la producción de cultivos, específicamente en cultivos de regadío. ...
... Se basan en los cambios en su delgadas placas de marco de cristal de cuarzo piezoeléctrico en espacios circundantes (Singh & Singh, 2020). ...
En este documento se desarrolla una perspectiva general de la Agricultura 4.0, resaltando
la eficiencia de las nuevas tecnologías, enfocando su adaptación y adopción en busca
de lograr la transición desde una agricultura convencional hacia sistemas productivos
inteligentes e innovadores. También se abordan los principales riesgos que van desde
los estrictamente técnicos hasta los del comportamiento del mercado o las decisiones
gubernamentales.
Para abordar estos temas el documento se divide en cuatro capítulos. El primero
presenta en contexto, una historia sucinta de la agricultura contada principalmente
desde los avances tecnológicos que ha experimentado. En el segundo se enumeran
algunas de las principales herramientas tecnológicas actuales como sensores, robótica,
nanotecnología, sistemas de información geográfica, Big Data e Internet de las Cosas,
cuya implementación mejoraría significativamente la productividad agropecuaria. En el
tercero se exponen algunas de las herramientas 4.0 disponibles para sistemas de riego que
están en capacidad de enfrentar el cambio climático y mejorar el rendimiento productivo
vía optimización de recursos hídricos. Por último, en el cuarto capítulo se habla acerca de
tecnologías 4.0 en la edafología donde se tiene en consideración la importancia del suelo
como soporte vital para el desarrollo de las diferentes actividades de producción.
... As the world population grows (Lidicker, 2020;Tamburino et al., 2020), one of the main challenges of agriculture is to increase food production (Fu et al., 2020;Singh and Singh, 2020) with significant reductions in environmental impacts (Pittelkow et al., 2014) and socioeconomic implications (Ofosu et al., 2020). Agriculture is a key factor in the economic sector for the Gross Domestic Product (GDP) growth in most countries (Abioye et al., 2020). ...
... For agriculture to achieve the desired impact on the quantity and quality of food production, it is necessary to know how to use the emerging technologies (Boursianis et al., 2020;Singh and Singh, 2020;Rose et al., 2021) for each moment in the agricultural production chain. SLR enabled identifying the leading technologies currently incorporated in agriculture 4.0 to detect problems in planting, identifying areas affected by pests, indicating appropriate treatments, among other benefits. ...
Agriculture 4.0 upgrades traditional production methods and world agriculture strategies to an optimized value chain using a range of emerging technologies that enhance disruptive solutions at all stages of the agricultural production chain. Due to the complexity of the changing farm ecosystem, the new technological revolution's benefits will not be shared evenly. It is necessary to understand the problems and challenges that need to be addressed so that all countries fully benefit from the potential of agriculture 4.0. This study aims to contribute to the development of agriculture 4.0 by identifying descriptions, technologies, barriers, advantages, and disadvantages. Three independent researchers carried out a Systematic Literature Review based on the Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses. After applying the inclusion and exclusion criteria pre-established in the Scopus, Science Direct, and Web of Science databases, 50 articles were selected for analysis. As a result, it was possible to identify the descriptions of agriculture 4.0, propose a definition, and present a compilation of approaches related to the term. Technologies of agriculture 4.0, responsible for revolutionizing and impacting how commodities are produced, processed, traded, and consumed, were also surveyed. Moreover, the barriers that hinder the development of agriculture 4.0 and that limit its progress are listed. The barriers were classified into five dimensions: technological, economic, political, social, and environmental. These are issues that need to be resolved in different areas to achieve a larger scale in countries looking to implement agriculture 4.0. Finally, this study's findings support actors in the agricultural production chain and pave the way for the successful development of agriculture 4.0. Besides, research helps broaden the inclusive debate that can shape the introduction of agriculture 4.0.
... The passive sensors are in need of an external source of light, like the sun. However, the active sensors are operated by their source of view of different wavelengths or a specific wavelength [9]. The relationship between the visible light and the chlorophyll content provides plant details. ...
... It will lead to overcoming the limitations of the sensor output. It is essential to monitor the leaves, which should not be covered by water molecules or dews, which may change the reflectance [9]. ...
Agriculture forms the major part of our Indian economy. In the current world, agriculture and irrigation are the essential and foremost sectors. It is a mandatory need to apply information and communication technology in our agricultural industries to aid agriculturalists and farmers to improve vice all stages of crop cultivation and post-harvest. It helps to enhance the country’s G.D.P. Agriculture needs to be assisted by modern automation to produce the maximum yield. The recent development in technology has a significant impact on agriculture. The evolutions of Machine Learning (ML) and the Internet of Things (IoT) have supported researchers to implement this automation in agriculture to support farmers. ML allows farmers to improve yield make use of effective land utilisation, the fruitfulness of the soil, level of water, mineral insufficiencies control pest, trim development and horticulture. Application of remote sensors like temperature, humidity, soil moisture, water level sensors and pH value will provide an idea to on active farming, which will show accuracy as well as practical agriculture to deal with challenges in the field. This advancement could empower agricultural management systems to handle farm data in an orchestrated manner and increase the agribusiness by formulating effective strategies. This paper highlights contribute to an overview of the modern technologies deployed to agriculture and suggests an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Besides, it elucidates the problems, specific potential solutions, and future directions for the agriculture sector using Machine Learning and the Internet of things.
... Whilst potentiometric pH sensors have several advantages, they are inherently challenging to implement on a large-scale under real-world conditions. This includes the need to improve manufacturing accuracy and precision to reduce batch by batch variation in sensor performance, the necessities to calibrate these sensor under the environmental conditions in the field, and similarly, to assess sensor sensitivity, stability, drift, and durability of the sensing element over time under real-world conditions (Pal et al., 2024;Singh & Singh, 2020). Of particular interest to this study is the potentiometric pH sensor recently developed by Manjakkal et al. (2019) from the bendable electronics and sensing technologies (BEST) that is composed of a graphite-polyurethane (G-PU) sensitive electrode with a silver (Ag)/Ag chlorine (Cl) reference electrode adhered to a small piece of cloth. ...
Following an abiotic or biotic stress, the pH of the extracellular space or the apoplast of a plant can change dramatically, such as wounding that causes an increase in pH or alkalinization. In this proof‐of‐concept‐study, a newly developed carbon‐based bendable potentiometric sensor was tested for the first time in vivo on common bean (Phaseolus vulgaris L.). The sensor was able to detect the same magnitude and direction of pH change after wounding as the conventional infiltration‐centrifugation method in five out of eight tested common bean cultivars. This highly scalable, non‐destructive, and cheap carbon‐based sensor could be used in the future within plant breeding to screen large populations of plants for responses to plant stresses.
... Notably, although MEMS/NEMS have been employed in several applications within the agricultural sector, their utilization in agriculture, especially in crop cultivation, remains relatively limited compared to other industries. However, due to the demand for improving agricultural processes and the widespread use of the Internet of Things (IoT) in the future, it is anticipated that there will be a high demand for small-sized, low-cost, low-power consumption, and easily mass-produced devices (Singh and Singh, 2020). Hence, ample opportunities exist for further advancements. ...
As the source of data acquisition, sensors provide basic data support for crop planting decision management and play a foundational role in developing smart planting. Accurate, stable, and deployable on-site sensors make intelligent monitoring of various planting scenarios possible. Recent breakthroughs in plant advanced sensors and the rapid development of intelligent manufacturing and artificial intelligence (AI) have driven sensors towards miniaturization, intelligence, and multi-modality. This review outlines the key technologies in developing new advanced sensors, such as micro-nano technology, flexible electronics technology, and micro-electromechanical system technology. The latest technological frontiers and development trends in sensor principles, fabrication processes, and performance parameters in soil and different segmented crop scenarios are systematically expounded. Finally, future opportunities, challenges, and prospects are discussed. We anticipate that introducing advanced technologies like nanotechnology and AI will rapidly and radically revolutionize the accuracy and intelligence of agricultural sensors, leading to new levels of innovation.
... Smart agriculture, through the inculcation of information communication technology manifested in the management of farms and other areas of implementation of clean and efficient agro-industry processes, paves the way for a sustainable future of food production for the growing population worldwide. In terms of the sustainable development goals (SDGs) of the United Nations (UN), smart agriculture, in its goal to have a high and clean increase in yields, directly and certainly addresses SDG number two, which is zero hunger [6]- [8]. ...
The study aims to determine the levels of soil parameters such as soil pH, macronutrients, and micronutrients. After determining said parameters, the system appropriately recommends crops and fertilizers suitable for the soil samples. For soil pH and macronutrient levels, i.e., nitrogen, phosphorus, and potassium, these parameters can be detected using the soil test kit. Meanwhile, for soil micronutrients, i.e., copper, iron, and zinc, there is a need for the development of appropriate assays for colorimetric processes that can be done for the appropriate determination of said micronutrients. Comparison of available machine learning such as support vector machine algorithm, naïve Bayes algorithms, and K-nearest neighbor algorithm is a must to determine the well-fit algorithm that is considered fast and has high predictive power in classification and regression. The outputs of the colorimetric and spectrometric processes are the inputs in the machine learning activities intended for crop and fertilizer recommendation.
... Sensors play a crucial role in modern agriculture, enabling farmers to make more intelligent and scientific decisions through real-time monitoring and data collection. Currently, soil sensors [16], meteorological sensors [17], crop sensors [18], mechanical sensors [19], and water quality sensors [20] are widely deployed in agricultural production. Integrated Internet of Things (IoT) technologies in Agriculture 4.0 effectively utilize various sensors to enable real-time monitoring and precise control over various aspects of agricultural production [21,22]. ...
Tractors are among the most widely used machinery in agriculture. However, low-frequency vibrations during tractor operations pose significant health risks to drivers, such as musculoskeletal disorders, and negatively impact ride comfort. Current approaches rely on offline comfort prediction models, which lack real-time feedback, making them unsuitable for practical field applications. To address this gap, we propose a real-time recommendation system based on Internet of Things (IoT) and Machine Learning (ML) to enhance the driving comfort of agricultural tractors. Our low-cost IoT-enabled solution is compatible with existing tractors, requiring no expensive intelligent upgrades. Using the XGBoost model for ride comfort prediction, we achieved superior performance (R
2
= 0.96, RMSE = 0.015) compared to other ML models. Additionally, the Particle Swarm Optimization (PSO) algorithm is employed to recommend optimal operational parameters, reducing the ride comfort value (OVV) by 6.67% in real-time experiments. This study highlights a scalable, data-driven approach for improving tractor comfort and offers a reference for intelligent control strategies in Agriculture 4.0.
... Furthermore, by tracking soil conditions and supplying crucial information for maximizing crop development, soil-based sensors and plant monitors can further improve precision agriculture (Yin et al., 2021). In advanced vertical farming, the integration of sensors and actuators is vital for automation, efficiency, and simplified management (Singh and Singh, 2020). Actuators interact with environmental aspects and gather crucial data on temperature, pH alterations, light, decreasing the need for ongoing human intervention (Saad et al., 2021). ...
“Biomimicry” is an acronym used to describe how people looked at nature for inspiration to tackle a variety of problems. The modern problems of fast-increasing urbanization, land degradation, climate change, pandemics, loss of biodiversity, and widespread use of pesticides and fertilizers seriously threaten our food supply chain. There is a growing consumer demand for nutrient-dense, flavourful plant-based cuisine with minimal environmental impact. Moreover, a considerable portion of food roughly 24% is lost before it reaches consumers, partly as a result of poor quality and protracted supply chains. Researching new methods of producing food is essential since, by 2050, there will be more than 9.7 billion people on the planet, 70% of whom will reside in cities. Vertical farming (VF), which relieves pressure on conventional agricultural land by using vertical space instead of horizontal expansion, is growing in popularity as a solution to these problems. Because VF incorporates soil-less growth techniques, it is well-suited for urban environments. This strategy may help to produce more premium products, such as fruits, vegetables, flowers, and herbs. It may also help to produce cosmetics and medications made from plants. Vertical farming, is becoming more favoured as an alternative to traditional agriculture, and provides avenues for enhancing sustainable food production given the growing challenges of climate change and population growth.
... Agricultural sensors have entered the agricultural field in an all-round way, providing sufficient power for the development of agricultural robots. Thanks to sensors [11], it is possible to eliminate the limitations of natural factors such as the weather; realize the remote scientific monitoring of fields, greenhouses, aquatic products, and animal husbandry; and effectively reduce labor consumption [12]. Combining agricultural sensors with a scientific analysis makes it easier for the entire agricultural sector to resist disasters and risks and improve productivity [13]. ...
Agriculture plays a crucial role in development, especially in low-income countries where the sector is large in terms of both aggregate income and total labor force [...]
... The food industry includes several stakeholders, from farmers to food production and processing companies [32]. Digital transformation is an essential tool for addressing the challenges and opportunities in the food and agricultural industry [47,48]. This tool not only contributes to meeting the increasing demand for food but can also promote more ecological and responsible agriculture that fulfills the needs and values of society [31,49,50]. ...
The digital revolution is reshaping various aspects of society, including having a profound impact on food security and the advancement of Sustainable Development Goals (SDGs). This study investigates the relationship between digital transformation, quantified through the components of the Digital Economy and Society Index (DESI), and SDGs related to food (SDG1, SDG2, SDG3, and SDG10), along with the overall SDG Index score. The data used for investigation are sourced from reports issued by the European Commission concerning DESI, as well as the SDG reports for the period from 2017 to 2022. The paper elucidates how different components of digitalization, such as connectivity, digital skills, internet usage, and digital public services, influence the attainment of food security objectives and broader sustainable development targets using structural equation modeling and cluster analysis. The findings underscore the pivotal role of digital technologies in enhancing poverty alleviation, health and well-being, and, in particular, mitigating inequality. This study contributes to understanding the complex relationship between digital transformation and food security, offering insights for policymakers, practitioners, and stakeholders aiming to leverage technology for advancing SDGs and fostering a more equitable and sustainable future.
... The variability present in the field, which today can be observed and defined through management zones located even in small cultivation areas, can be explained through the learning acquired throughout history (Molin et al. 2015). In this sense, precision agriculture is a management strategy that combines advanced sensors associated with information technology to improve the productivity and quality of modern agriculture (Singh and Singh 2020). ...
... For fruit crops and some cut flower crops, interest in the adoption of sap-flow sensors is increasing. These sensors estimate plant transpiration based on different approaches; stem heat balance and heat pulse methods are considered the most suitable approaches for greenhouse crops (Singh and Singh, 2020). This is a promising approach for soilless substrate cropping where the evaporative component of ETc is generally negligible. ...
... RGB Table 3 Contrastive study of Active and Passive sensor [43] Kind of RS Operational frequency For measuring radiation intensity within various wavelengths bands, furthermore remote sensing of particular geophysical parameters sensors mounted on UAV cameras reproduce captured images of the same effect to the human eye. Thus, quicker observations of the entire field by captured images, aerial videos at a single instance, and GPS data root problem identification are possible without ruining the whole land [69]. ...
In adopting state-of-the-art technologies, a domain known in time is Agriculture for fertility optimization, expense saving, assistance, and environmental safeguard. In this aspect, deploying UAVs remains a modern example in the Agriculture sector, encompassing various possibilities at ease. Concerning innovations, UAV (drone) invention remains the standard talked-about technology. UAV’s broader view includes drone ranges like micro, mini, small, and medium aerial vehicles. Initially developed applications for the military now have used assistance like firefighting, courier services, mob surveillance, facial recognition, and many more. Within the paper, UAV applications in agriculture are of primary interest, with a notable centre of attention being crop farming. This paper presents a comprehensive survey on UAV types, crop health, agricultural sensors, remote sensing with UAVs, animal marking, pesticide sprinkling, other possible agricultural use, and Precision Agriculture. We explore the approach utilized for each UAV type application and the UAV technical characteristics and payload. Beyond uses, UAV’s services and implied advantages within agriculture are further exhibited beside talks on business correlated hurdles and additional apparent difficulties limiting the broad adoption of UAVs into agriculture. The belief of the work done in the paper will prove worthwhile to Researchers working on an amalgamation of UAVs in PA. With our work, they can make a necessary spontaneous understanding of the agricultural aspect and how it should work. Those farmers attempting modernized agricultural method optimization approaches on various levels by this work benefit from new ways of using UAVs within their farming. UAV businesses exploring innovative UAV use capacities can examine the future concerning the UAV run in agriculture furthermore strengthen their endeavours within the trend.
... Precision agriculture, a development in mechatronics, is already playing a significant role in agricultural industries, where it has minimized labor requirements and decreased crop production costs by maximizing output. The main benefit of mechatronics system integration in agriculture, however, is a doubling in efficiency compared with manually controlled machines, and this has enabled a revolution in how agricultural crops are established, managed, and harvested [13][14][15][16]. ...
Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Impressive advances have been made since then in developing systems for use in modern agriculture. The aim of this study was to review smart mechatronics applications introduced in agriculture to date, and the different areas of the sector in which they are being employed. Various literature search approaches were used to obtain an overview of the current state-of-the-art, benefits, and drawbacks of smart mechatronics systems. Smart mechatronics modules and various networks applied in the processing of agricultural products were examined. Finally, relationships in the data retrieved were tested using a one-way analysis of variance on keywords and sources. The review revealed limited use of sophisticated mechatronics in the agricultural industry in practice at a time of falling production rates and a dramatic decline in the reliability of the global food supply. Smart mechatronics systems could be used in different agricultural enterprises to overcome these issues.
... Temperature and humidity are among the most important weather factors which directly affect the health and growth of all types of crops. Correct measurement of these environmental factors is helping the farmer adjust the quantity of fertilizer and water [18]. Various types of temperature, humidity sensors are available which helps the farmers to measure and monitor the levels of humidity and temperatures of their fields and greenhouses. ...
Drones are now a days emerging as a component of precision agriculture along with contributing to sustainable agriculture. The use of advanced technologies such as drone in agriculture offer potential for facing several major or minor challenges. The major applications of drone in agriculture are spraying, irrigation, crop monitoring, soil and field analysis and bird control. The objective of this paper is to review the latest trends and applications of leading technologies related to agricultural UAVs equipment, and sensors development. And also, the use of UAVs in real agricultural environments. Based on the literature, found that a lots of agriculture applications can be done by using Drone. In the methodology, we used a comprehensive review from other researches in this world. Furthermore, the future development of agricultural UAVs and their challenges are considered. In this review paper, summarizes the available agricultural drones and applications of UAVs for Precision Agriculture using different sensors to evaluated agricultural parameters such as NDVI, vegetation index, NIR, nutrient disorder using sensors like RGB, digital camera, multispectral and hyperspectral sensors and to reduce the wasting of water and chemicals quadcopter, hexacopter UAVs could be used.
... To include growing quantities of integration, sophistication, robustness, intelligence, feedback, have been targeted [10][11][12]. Because, the term "mechatronics" was created from the word's "mechanism", "computer", "control theory", and "electronics" [13]. The results of mechatronics in agriculture to expand the range of production from subsistence to commercial production, processing, packaging, storage, and delivery, reduce human drudgery. ...
Smart mechatronics system in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Since then, impressive advances have been made smart mechatronics systems. Furthermore, smart mechatronics systems are promising areas, as results, we were intrigued to learn more about them. Consequently, the purpose of this study was to examine the smart mechatronic systems that have been applied to agricultural areas so far, with inspiration from smart mechatronic system in other sectors. To get an overview of the current state of the art, benefits and drawbacks of the smart mechatronics systems, various approaches were investigated. Moreover, smart mechatronic modules, and various networks applied in agriculture processing were examined. Finally, we were explored how the data retrieved using the one-way analysis of variance related to each other. The result showed that there were strong related keywords for different journals. The virtually limited use of sophisticated mechatronics in the agricultural industry, and at the same time, the low production rate, the demand for food security has fallen dramatically. Therefore, the application of smart mechatronics system in agricultural sectors would be taken into consideration in order to overcome these issues.
... In recent years, agricultural sensors developed based on microelectronic system (MEMS) technology have been popularized and applied in agricultural science and technology due to their economic, accurate and sensitive characteristics. Using advanced sensors and information technology has become a method to improve the productivity and quality of modern agriculture [26]. The soil sensor market is huge. ...
Agricultural sensors are essential technologies for smart agriculture, which can transform non-electrical physical quantities such as environmental factors. The ecological elements inside and outside of plants and animals are converted into electrical signals for control system recognition, providing a basis for decision-making in smart agriculture. With the rapid development of smart agriculture in China, agricultural sensors have ushered in opportunities and challenges. Based on a literature review and data statistics, this paper analyzes the market prospects and market scale of agricultural sensors in China from four perspectives: field farming, facility farming, livestock and poultry farming and aquaculture. The study further predicts the demand for agricultural sensors in 2025 and 2035. The results reveal that China’s sensor market has a good development prospect. However, the paper garnered the key challenges of China’s agricultural sensor industry, including a weak technical foundation, poor enterprise research capacity, high importation of sensors and a lack of financial support. Given this, the agricultural sensor market should be comprehensively distributed in terms of policy, funding, expertise and innovative technology. In addition, this paper highlighted integrating the future development direction of China’s agricultural sensor technology with new technologies and China’s agricultural development needs.
... However, the adoption of modern farming machinery in these economies is often slow, partly due to farmer perception towards this technology. The perception of farmers towards farming machinery has a significant impact on their adoption decisions (Singh & Singh, 2020). Farmers who view farming machinery as a positive tool are more likely to adopt the technology than those who see it as a threat to their livelihoods (Koubi, 2019). ...
The adoption of advanced agricultural technologies is crucial in reducing poverty and improving food security on the developing economy countries. Despite the recognition of its importance, smallholder farmers in developing nations often face obstacles in adopting new technologies, leading to slow uptake. To better understand the factors that impact the use of agricultural technology in these nations, this study seeks to provide insights through a review of previous research on technology adoption. The results of the study highlight several key elements that determine whether or not agricultural technology is adopted, including factors related to the technology itself, the economic situation of farmers, the institutional and organizational affiliations of the farmers, and unique household factors. In order to fully comprehend the complexities involved in the adoption of agricultural technology, future research is recommended to include the perspectives and experiences of farmers. By considering their perceptions of new technologies, the range of factors influencing. The purpose of this paper is to gain a deeper understanding of the challenges faced by smallholder farmers in adopting new technologies and to identify opportunities for improvement. By considering their perceptions of new technologies, the range of factors influencing technology adoption can be broadened, providing a more comprehensive understanding of the subject.
... RGB sensörler ile tek bir örnekte tüm alanın görüntüleri ve hava videolarının yakalandığı ortomozaik haritalar oluşturulabilmektedir. Böylelikle, daha hızlı gözlem yapılması sağlanmakta ve bir sorun olması durumunda tüm alanı etkilemeden problemin köküne inilebilmektedir (Singh ve Singh, 2020). Ayrıca değişen hava koşullarında, tarımsal faaliyetlerin verimli ve etkili bir şekilde ayrıntılı olarak incelenmesine yardımcı olmaktadır (Maddikunta vd., 2021). ...
Agriculture is both a vital sector of activity for the sustainability of life and strategic field of activity for provides raw materials to non-agricultural sectors and contributes to national income and employment. The use of new techniques or devices in agriculture, which emerged with the rapid development of technology, makes agricultural applications easier and more effective. The use of drones in agriculture, which is one of the most popular technological developments in recent years, has become widespread and its use is increasing even more with the addition of new application areas. The popularity of drones and their use in agriculture also attract the attention of those from different disciplines other than agriculture. Due to the insufficient technical knowledge of those in different disciplines on agriculture, false information or ineffective use of drones in agriculture may occur. In this study, information is given about the drone and its components, the advantages and disadvantages of the drone, the cameras and sensors that can be used with the drone. Then, the use of drones in agriculture today is explained with sample applications and predictions are presented with the use of drones in agriculture in the future. In addition, explanations were made about the use of drones in agriculture, some misinformation and ineffective use.
... Modern agriculture has been continuously compelled to advance and to make use of sustainable technologies. Such technologies include genetic breeding tools, efficient-release fertilizers, soil management strategies, intelligent use of water and agrochemicals, internet of things, crop and weather monitoring, nanotechnology and integrated techniques of farm administration among others (Ali et al., 2018;Chowdhury et al., 2019;Leakey et al., 2019;Lowenberg-DeBoer & Erickson 2019;Pandey et al., 2019;Saiz-Rubio & Rovira-Má s, 2020;Singh & Singh, 2020;Devlet, 2021). ...
In modern agriculture, there is a growing need for increasing crop efficiency while minimizing environmental impacts. The use of high-efficiency light supplementation to enhance plant development is limited for high-productive crops at field conditions (outdoor). This study evaluated the soybean plant’s yield responses in an open commercial area (field scale) cultivated under conditions of artificial light supplementation. A commercial irrigated (pivot) area received an illumination system for light supplementation (LS) in its inner pivot spans. About 40 hours of LS were applied to the plants during the soybean crop cycle. The area’s outer pivot spans did not receive light supplementation (nLS). The internode number, the plant height, the pods per plant were evaluated weekly to compute the area under the progress curve (AUPC). The grain yield at harvest was also assessed. The AUPC of the internode number, plant height and pods per plant were positively affected by the LS treatment. The regular soybean cycle (nLS) is about 17 weeks; however, the LS harvest occurred three weeks later. Light supplementation increased soybean grain yield by 57.3% and profitability by 180% when compared to nLS. Although light supplementation at field scale poses a challenge, it is now affordable since sustainable field resistant technologies are now available. The present study is the first known report of light supplementation used to improve soybean crop production at field scale.
... Advances in technology have led to the emergence of devices capable of analysing in real-time and transmitting the results online for rapid decision making. So much so that, the use of Precision Agriculture is the only viable way to manage the needs of such an overpopulated world (Singh & Singh, 2020). ...
Imaging techniques have revolutionised the way quality is assessed in food products. Using cameras, it is possible to estimate not only the chemical composition of a product but also its geometric distribution. However, the limited range of detectors implies the use of different measuring equipment. The presence of small and discrete samples or very heterogeneous samples makes joining both sets of data a complicated task. This work arises from the need to merge images with colour information and NIR spectral information on grape samples and derivatives. An application has been created under MATLAB to join this type of images so that it is possible to simultaneously extract the colour and/or spectral information of each pixel or object present in the image. Although the software can be used in a wide range of applications, it has been successfully applied to grape and grape seed samples. In red grape bunches, it was possible to evaluate individually grapes and notice differences due to changes in visible and infrared regions at the same time. In the case of white grape seeds, it was proved that merged images were better to discriminate between varieties than the single CIELAB or spectral images.
... Also, the costs of the RGB sensor based cameras are relatively affordable, light weight and extremely good at creating orthomosaic maps that captures images and aerial videos of the entire field at a single instance. This enables to take quicker observations and after entering the geographical data into the GPS, one can immediately get into the root of the problem without affecting the entire field [29]. The RGB cameras thus help in detailed inspection of agricultural assets efficiently and effectively in varying weather conditions. ...
In the next few years, smart farming will reach each and every nook of the world. The prospects of using unmanned aerial vehicles (UAV) for smart farming are immense. However, the cost and the ease in controlling UAVs for smart farming might play an important role for motivating farmers to use UAVs in farming. Mostly, UAVs are controlled by remote controllers using radio waves. There are several technologies such as Wi-Fi or ZigBee that are also used for controlling UAVs. However, Smart Bluetooth (also referred to as Bluetooth Low Energy) is a wireless technology used to transfer data over short distances. Smart Bluetooth is cheaper than other technologies and has the advantage of being available on every smart phone. Farmers can use any smart phone to operate their respective UAVs along with Bluetooth Smart enabled agricultural sensors in the future. However, certain requirements and challenges need to be addressed before UAVs can be operated for smart agriculture-related applications. Hence, in this article, an attempt has been made to explore the types of sensors suitable for smart farming, potential requirements and challenges for operating UAVs in smart agriculture. We have also identified the future applications of using UAVs in smart farming.
... There have been scientific reports with examples of attempts to solve this problem with optical sensors [21]. There have also been experiments on piezoelectric sensors placed inside the distributor head of the seed drill [22] and tests of the sensitivity of various sensors [21,23]. Patent literature provides an example with an attempt to use capacitive sensors-US Patent 4782282 A. However, the experiment showed that this method was not promising due to slight changes in the capacitance of the sensors. ...
Samples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) transporting seeds in the pneumatic conduit was adjusted to sowing. The resulting graphic database enabled the distinction of six classes of seeds according to their quality and sowing speed. The database was prepared to build training, validation and test sets. The neural model generation process was based on multi-layer perceptron networks (MLPN) and statistical (machine training). When the MLPN was used to identify contaminants in seeds sown at a speed of 15 m/s, the lowest RMS error of 0.052 was noted, whereas the classification correctness coefficient amounted to 0.99.
... Modern agriculture is continuously being pressed to develop and apply sustainable technologies. Such technologies are related to, but not only, breeding genetic tools, e cient-release fertilizers, nanotechnology, soil management strategies, smart use of the water, internet of things, crop and weather monitoring and integrated techniques of farming administration [1][2][3][4][5][6][7]. ...
In modern agriculture, there is a growing need for cropping efficiency e low environmental impacts. Diverse technologies are becoming available in a recent wave of modernization and integration of knowledge. The use of high-efficiency light supplementation to plant development is scarce to high-productive crops at field conditions (outdoor). The objectives of this study were to evaluate soybean plant and yield responses in an open commercial area (field scale) cultivated with artificial light supplementation. A commercial irrigated (pivot) area received an illumination system for light supplementation (LS) in the inner pivot spans. The light applied was a composition of blue, green and red bands. The outer pivot spans did not receive light supplementation (nLS). About 40 hours of LS were applied to the plants during the soybean crop cycle. Internode number, plant height, pods per plant were weekly evaluated to compose the area under the progress curve (AUPC). The grain yield was also evaluated at harvest. Analysis of variance and test of averages were used to evaluate the data. The AUPC of the internode number, plant height and pods per plant were 15.6, 23.3 and 25.3% higher than for the LS treatment. The regular soybean cycle (nLS) was about 17 weeks; however, the harvest of the LS treatment happened three weeks later. The grain productivity of the nLS was about 4,500 kg ha ⁻¹ (75 bags), and of the LS treatment was about 7,080 kg ha ⁻¹ (118 bags) - 57.3% superior. Light supplementation at field scale is a challenge; however, affordable and field resistant technologies are now accessible. The present study is the first report of light supplementation used to improve soybean crop production at field scale. The possibility of using light regulation as an additional technique for increasing yields and sustainable production are also discussed.
... Their poor water retention capacity calls for precision-irrigation to minimize the waste of valuable water resources (Cifre et al., 2005). In such scenarios it becomes insufficient to assess irrigation thresholds only based on soil water balance (Jones, 2006;Ihuoma and Madramootoo, 2017), requiring novel techniques of early plant water stress detection (Oletic and Bilas, 2020;Centeno et al., 2010;Savi et al., 2019;Singh and Singh, 2020). ...
In cultures like grapevine, growth, yield and fruit quality depend on precise regulation of water stress using precision irrigation. In arid climates with seasonal droughts, karst areas and porous stony soils it is difficult to assess irrigation thresholds only based on soil water balance, requiring novel, plant-based techniques of early drought stress detection. In plants, water stress causes an increase in evapotranspiration rate, subjecting the water in the xylem conduits to high tension. This creates cavitations, leading to xylem embolization, loss of hydraulic conductivity and possibly plant death. A promising emerging technology for automated, non-invasive field monitoring of xylem cavitations is acoustic emission (AE) testing. Passive piezoelectric sensors on trunk are used to capture intermittently appearing AE, occurring in ultrasonic frequency-band from 100 kHz to 1 MHz. To make AE suitable as an input for precision irrigation, we present a novel AE time-frequency feature set for discrimination between normal and drought stress conditions. The proposed feature extraction method entails software-compensation of sensor's frequency response, enabling the wide-band frequency-analysis with miniature resonant high-frequency piezoelectrics. The signal analysis revealed three characteristic groups of AEs, differentiated by their spectral composition: single-component, multi-component, and broadband AEs. In conditions of increased water stress, single-component emissions cluster around 200 kHz, and multi-component emissions group between 200 and 600 kHz, forming discriminant time-frequency signatures. The study entails an extensive set of laboratory pot-plant dehydration experiments on four different grapevine cultivars, verifying the selectivity, sensitivity and repeatability of the extracted feature set. It proved sensitive enough for detection of drought stress corresponding to moderate leaf water potentials ranging between −0.7 and −1.2 MPa.
... N. Singh et al. [3] studied the contribution of sensor based technologies over the agriculture. It found that as per application requirements, different types of sensors are required and its deployment and operational cost is changed accordingly. ...
Integration of the communication technology with traditional cultivation process, known as Precision Agriculture or Smart Farming. Usage of Wireless Sensor Networks (WSN) to monitor the agriculture activities can improve farming experience but it requires long terms network operations and due to limited power backup, optimal energy consumption becomes a major issue for WSN based applications. To resolve this issue, in this paper, an optimal energy consumption scheme is introduced and its performance is analyzed using different performance parameters
In precision agriculture, farmers adopt sensors to improve decision-making, monitor the conditions of the soil, optimize fertilizer and herbicide application, and enhance overall yields from farms. Traditional sensors are based on IoT components that relay signals from soil and the farm to servers where data management operations improve decision-making. However, cyber threats, high costs of maintenance, and poor connectivity to the internet challenges hinder the adoption of the sensors. To address such shortcomings, quantum sensors, based on quantum mechanics principles, are adopted in agriculture based on their higher sensitivity and chemical inertness. The focus of this review article was to investigate the different perspectives on how quantum sensors are used in agriculture. The review article assessed the potential applications of quantum sensors in agriculture, where 119 studies sourced from different scientific databases were examined. The insights showed that quantum sensors were applied in agriculture to enhance seedling growth and emergence due to the chemical inertness and solubility. Additionally, the sensors enhanced crop growth through the provision of nutrients and antioxidants to promote the growth of crops. The findings further indicated that sensors were used to detect harmful pollutants and chemicals from water sources and agricultural foods such as pumpkin seeds. Other applications included monitoring the soil fertility levels. The analysis also showed that the sensors were used as measuring instruments to evaluate photosynthetically active radiation (PAR) in evaluating plant photosynthesis. The recommendations from the review article advocate for the continued adoption of quantum sensors as they outperform the classical sensors due to their atomic-level sensitivity. However, in the future, scholars ought to focus on investigating methods that are cheaper to integrate quantum sensors in large-scale farming operations.
The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have introduced new challenges, particularly in cybersecurity. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks. Moreover, IoT services often handle sensitive user data, which could be exploited by malicious actors or unauthorized service providers. As IoT ecosystems expand, the convergence of traditional and cloud-based systems presents unique security threats in the absence of uniform regulations. Cloud-based IoT systems, enabled by Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) models, offer flexibility and scalability but also pose additional security risks. The intricate interaction between these systems and traditional IoT devices demands comprehensive strategies to protect data integrity and user privacy. This paper highlights the pressing security concerns associated with the widespread adoption of IoT devices and services. We propose viable solutions to bridge the existing security gaps while anticipating and preparing for future challenges. This paper provides a detailed survey of the key security challenges that IoT services are currently facing. We also suggest proactive strategies to mitigate these risks, thereby strengthening the overall security of IoT devices and services.
Flexible temperature and humidity sensors that can be used to observe accurate and concurrent variations in the
temperature and humidity of the microclimate of plants have a promising role in precision agriculture. These
flexible sensors can provide promising information for optimizing crop growth environment and fight against
abiotic stresses, enhancing crop yields. Herein, we summarize the importance and recent advances in flexible
temperature and humidity sensors of plants for precision agriculture applications. We highlighted the architecture,
working principle, sensing mechanism, and fabrication materials of plant sensors. Key temperature and
humidity sensing pathways in plants to respond to abiotic stresses at the molecular level are reviewed. Temperature
and humidity sensors’ major applications in intelligent and precision agriculture, current challenges,
and future prospects are also discussed. Lastly, the research direction for next-generation intelligent plant sensors
is briefly described. This review article will provide direction and intuition to scientists to further research in
flexible plant sensors to address the current challenges in smart and precision agriculture.
This chapter aims to offer insight into the recent technological, societal and channel research advancements in agribusiness. More in detail the study introduces the concept of sustainability and potential application in agribusiness and explores the role of technology in the transition process towards more sustainable approaches. A specific focus is done on agriculture 4.0 as a strategy able to offer farmers the opportunity to save resources and reduce waste in a view of the circular economy paradigm.
The precision of trait identification and selection is essential for enhancing genetic gain in any crop improvement program. This requires an understanding of the components underlying a trait in a particular environment. As environment plays a crucial role in the expression of phenotype, correct characterization of complex traits requires that the tools and techniques emerging in this area of plant phenotyping on a large scale or phenomics be reviewed critically, and used extensively in plant breeding programs. This chapter aims to discuss advances in sensors, information technology (IT), data extraction tools, and system integration strategies in the area of plant phenomics. Cost-effective technologies in assessing plant morphology and physiology, preferably in a non-destructive manner, repeatability across whole populations and throughout development options are also discussed.KeywordsHigh-throughput phenotypingShovelomicsNon-destructive phenotypingFruit traits
Vertical farming has emerged as a promising solution to cope with increasing food demand, urbanization pressure, and limited resources and to ensure sustainable year-round urban agriculture. The aim of this review was to investigate the evolving technological landscape and engineering considerations, with a focus on innovative developments and future prospects. This paper presents technological trends in vertical farming, covering advances in sensing technologies, monitoring and control systems, and unmanned systems. It also highlights the growing role of artificial intelligence (AI) in contributing to data-driven decision-making and the optimization of vertical farms. A global perspective on vertical farming is presented, including the current status and advanced technological trends across regions like Asia, the USA, and Europe. Innovative concepts and upcoming enterprises that could shape the future of vertical agriculture are explored. Additionally, the challenges and future prospects of vertical farming are also addressed, focusing on crop production limitations, environmental sustainability, economic feasibility, and contributions to global food security. This review provides guidance on the state of vertical farming, technological advancements, global trends, challenges, and prospects, offering insights into the roles of researchers, practitioners, and policymakers in advancing sustainable vertical agriculture and food security.
Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures.
Artificial intelligence (AI) is well known for diverse applications in many fields including agriculture. The use of AI in the agricultural field has allowed for optimization in real-time and with less cost and effort. This chapter presents a bibliometric analysis of the future of AI in agriculture. This analysis was carried out using the online Scopus tool and VOSviewer software. The outputs show an increasing trend of documents from 1979 to 2021 and 2020 is the most productive year in terms of quantity. Computer science, engineering, Agricultural and Biological sciences, and environmental science are the main subjects while the United States, China, and India are the most influential in the field. United States, China, and India are the most influential in the field, while article and conference papers are the top two doctypes. For the trend, research in this field is turning toward machine learning, the Internet of things, deep learning, agricultural robots, learning algorithms, big data, data analysis, image processing, support vector machines, and automation.
Industry 4.0 and its technologies can potentially increase business competitiveness in the age of digital transformation through the implementation of its technologies. These digital technologies are increasingly present in the food industry, characterizing the concept of Agriculture 4.0. This digital transformation is a reality; however, it is unclear which digital technologies are most useful for each industry sector. Therefore, this paper seeks to explore the current state of implementation of digital technologies in different industrial sectors and which digital technologies should be leveraged to increase the performance of the agribusiness system. To do so, we used secondary data from a large-scale survey of 28 industrial sectors, representing 2225 companies in the Brazilian industry. Analyzing the different industrial sectors allowed us to present a framework of digital transformation to boost food industry competitiveness towards Agriculture 4.0. The results show that the food industry usually uses only one digital technology, showing the need for simultaneous and joint investments in the other technologies presented in this research. Public policies must be directed to encourage the expansion of digital technologies in the food industry.
The energy hunger society drives for more electrical power consumption to raise the
human conveniences has put tremendous pressure on the existing power system (PS)
to function uninterruptedly. The continuous addition of non-linear loads brings a heavy
inrush of fault current leading to transient instability in PS. To protect the PS and its
precious elements, fault current limiters (FCLs) are generally installed with a view to
limit those FCs before inviting severe damage and also quickly restore the PS to their
original state. Furthermore, since faults are inevitable, efforts can only be made to
minimize those damages by improving the FCLs. Herein, a brief history of FCLs,
concepts, working principles, developmental stages, and experimental data, along with
theoretical predictions, have been systematically presented. Finally, this article
elaborates on the current challenges of FCLs, future perspectives, and, most
importantly, their market potentials.
Agriculture 4.0 is the usage of advanced technologies for ensuring sustainability and efficient use of resources in agriculture, which can be accepted one of the biggest challenges of mankind. With Agriculture 4.0 technologies, it is aimed to raise productivity, reduce waste and costs. To ensure that agriculture 4.0 technologies are adopted by farmers as soon as possible, the perspective and perception of farmers to these technologies should be analyzed firstly. For this purpose, we propose a decision-making framework to measure farmers' view of Agriculture 4.0 technologies and to perform a prioritization study based on the perception of use among these technologies. Multicriteria decision analysis is also utilized to deal with all qualitative and quantitative factors in the decision process. Within the scope of this study, interval-valued spherical fuzzy numbers are used to model the vagueness in the process in the best manner and to be able to reflect the uncertainty arising from the usage of linguistic variables in the decision process. The SWARA and MAIRCA multicriteria decision making (MCDM) methods, which have been used frequently in the literature and are applied very successfully in MCDM problems, have been firstly extended by spherical fuzzy sets (SFSs) and the advantages for these methods have been utilized within the framework of fuzzy logic. The proposed method allows decision-makers to mirror their hesitations in decision-making using a linguistic evaluation scale established upon interval-valued SFSs. A comparative analysis based on the ordinary fuzzy sets is also performed for the obtained results and the clear superiority of the proposed approach is presented. In addition, the robustness of the model is tested with sensitivity analysis. After these analyzes it is obtained that this paper provides a unique perspective to the literature due to its originality in both the subject and adopted fuzzy methodology.
Thinking about sustainable actions for big cities is one of the problems of the twenty-first century. This demand is faced by cities in the different dimensions that make it up: social, economic, and environmental. Just as companies should think about adopting and investing in Environmental, Social, and Governance (ESG), cities need to become smart cities, an important step to transform themselves into sustainable cities with a higher quality of life. ESG, in companies, refers to the three central factors for measuring the sustainability and social impact of an investment process. Cities need to focus, in addition to modernizing their infrastructure, on strengthening the circular economy, investing more in education, reducing school dropout, which is closely linked to the area of security in cities, and based on these measures, seek balance in all areas of society. The concepts of smart cities and sustainable cities will be presented, showing the interconnection of both concepts. The purpose of this chapter is to show how to build smart cities and how they are transformed into sustainable cities. Thus, a systematic and integrative review was carried out in the Scopus database, in order to answer the following questions: (1) What are smart cities? (2) How to transform cities into smart cities? (3) What is the interconnection between the two concepts: smart cities and sustainable cities? (4) How do smart cities contribute to building a sustainable city? The results converge in the relationship between intelligence and sustainability, being the basis for a new type of city that directs its growth in an intentional, collaborative, and inclusive way to transform urban centers into a place with a higher quality of life. This chapter brings two examples of smart cities: one in Portugal and another in Brazil, explaining how the national legislation of each country and its social reality interfere in the construction of smart cities. The main innovation presented is the indicators for transforming smart cities into sustainable cities. For this purpose, the chapter is divided into eight sections, which are entitled: Introduction, Smart Cities, Sustainable Cities, Methodology, Relationship Between Sustainable Cities and Intelligent Cities, From Smart Cities Toward Sustainable Cities: Inclusion and Life Quality, Case Studies: Smart Cities from Brazil and Portugal, and Conclusions.
Smart farming is one kind of application of modern communication technologies in agriculture, leading to the third green revolution. The third green revolution will transform agriculture into a new dimension with Internet of Things (IoT), Big Data, and sensor technologies. These technologies, like robotics, unmanned aerial vehicles, machine learning, and artificial intelligence, increase crop quality. Today’s agriculture industry is precise, smarter, and data-centered. The Internet of Things is evolving rapidly, and IoT technologies have redesigned many sectors in the real world, including smart agriculture. These redesigned methods have changed the conventional agricultural practices and developed new opportunities for many researchers with different challenges. This chapter mainly highlights the IoT architecture’s capabilities post-pandemic in the real world and smart agriculture sensors’ potential and challenges while integrating the technology with conventional farming practices. Sensors were mostly available for agriculture applications such as soil identification, crop status, pesticide detection, etc. The IoT technology that helps sow crops, packing, and transportation is broadly explained in this chapter. Furthermore, the unmanned aerial vehicle (UAV) or drone usage is described broadly for applications such as surveillance of crop and crop yield. The types of sensors that are suitable for farming will be explained extensively in this subsection. The requirements of the UAV’s and future applications of using drones in smart farming are broadly discussed. Finally, based on this chapter, the researchers can identify smart agriculture and farming’s future trends.
Magnetic nanoparticles (MNPs) uniquely combine superparamagnetic performance with dimensions that are smaller than or similar size to molecular analytes. Recently, functionalized MNPs are predicted to be a driver for technology and business in this century and hold the promise of high performance materials that will significantly influence all aspects of society. Functionalized MNPs are creating new possibilities for development and innovation in different analytical procedures. Despite their participation in modern development, they are in their infancy and largely unexplored for their practical applications in analysis.
This book will provide quality research and practical guidance to analytical scientists, researchers, engineers, quality control experts and laboratory specialists. It covers applications of functionalized MNPs in all stages of analytical procedures. Their incorporation has opened new possibilities for sensing, extraction and detection enabling an increase in sensitivity, magnifying precision and improvement in the detection limit of modern analysis. Toxicity, safety, risk, and legal aspects of functionalized MNPs and the future of analytical chemistry with respect to their use is covered. The book provides an integrated approach for advanced analytical methods and techniques for postgraduates and researchers looking for a reference outlining new and advanced techniques surrounding the applications of functionalized nanomaterials in analytical chemistry.
Some non-digitized measuring instruments such as pointer meters are utilized by some farmlands by virtue of their high stability and accuracy, and the data collection depending on the human movements requires considerable time and effort and has a low technology level. Therefore, an intelligent pointer meter indication reading and automatic collection method is proposed using machine vision and wireless sensor network (WSN) technology. An intelligent pointer meter indication reading device consisting of an image acquisition module, an intelligent control center, a wireless transmission module, and a power module was developed, which could accomplish intelligent meter indication reading and automatic data uploading. Furthermore, a pointer meter indication reading algorithm for a resource-limited environment is proposed. Firstly, the pointer and the circle axis were recognized by using edge detection and the geometrical characteristics of the pointer meter, and then, the scales and scale values were determined using the statistical characteristics of the circles around the pointer’s vertex. Next, by utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm, we adjusted the scales and the scale values, taking full advantage of the pointer’s characteristic that the scales’ rotation angles changed linearly with the scale values, which were classified by an SVM taking in the HOG feature as the input. Finally, the indication was computed in terms of the relative position of the pointer between the adjacent scale values, and the accuracy was 94.2%. The proposed method was evaluated by comparing it with some newly published algorithms. The pointer detection method had stronger robustness and anti-interference capability than the image segmentation method and the skeleton extraction method. The Θ(n) value, indicating the complexity of the scale detection algorithm, was considerably smaller than the Θ(n²) value, denoting the modified central mapping method used mostly for scale detection. The energy consumption by each module was measured, and the results showed that the image acquisition module accounted for the largest energy consumption, 87.7%, which was more than eight times as much as the second-largest energy consumption, 10.3% (that of the wireless transmission module); the problem of power supply could be solved by using solar panels.
Background:
The presence and persistence of water on the leaf can affect crop performance and thus might be a relevant trait to select for or against in breeding programmes. Low-cost, rapid and relatively simple methods are of significant importance for screening of large populations of plants for moisture analysis of detached leaves. Leaf moisture can be detected using an electric circuit, where the resistance changes are proportional to the moisture of the measured surface. In this study, we present a protocol to analyse genotypic differences through the electrical properties of living or stored tissues, performed using a commercial device. Expanded and non-expanded leaves were compared to determine the effects of leaf maturity on these data. Two wheat genotypes that differ in tissue affinity for bound water were used to define the influence of water status.
Results:
The device indirectly estimates leaf moisture content using two electrodes applied to the leaf lamina of fresh and stored samples. Single moisture readings using this moisture meter had mean execution time of ~ 1.0 min. Exponential associations provided good fits for relationships between the moisture meter reading (MMR) and the electrical resistance applied to the electrodes. MMR normalised for the water/ dry matter ratio (MMRnorm) was lower for mature leaves of the water-mutant than those of wild-type, for the fully hydrated fresh leaves. MMR of fully mature leaves when partially dehydrated and measured after 10 min at 27 °C and 40% relative humidity was greater for the water-mutant than the wild-type.
Conclusions:
This case study provides a low-cost tool to compare electrical-resistance estimates of leaf moisture content, together with a promising and rapid phenotyping protocol for genotypic screening of wheat under standard environmental conditions. Measurement of changes in MMR with time, of fresh and partially dehydrated leaves, or of MMR normalised to tissue water content allowed for differentiation between the genotypes. Furthermore, the differences observed between genotypes that here relate particular to tissue affinity for bound water suggest that not only the free-water fraction, but also other water fractions, can affect these electrically estimated leaf moisture measures.
Precipitation is useful information for assessing vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using deep learning algorithms are promising for these purposes. Echo state network (ESN) and Deep Echo state network (DeepESN), referred to as Reservoir Computing (RC), are effective and speedy algorithms to process a large amount of data. In this study, we used the ESN and the DeepESN algorithms to analyze the meteorological hourly data from 2002 to 2014 at the Tainan Observatory in the southern Taiwan. The results show that the correlation coefficient by using the DeepESN was better than that by using the ESN and commercial neuronal network algorithms (Back-propagation network (BPN) and support vector regression (SVR), MATLAB, The MathWorks co.), and the accuracy of predicted rainfall by using the DeepESN can be significantly improved compared with those by using ESN, the BPN and the SVR. In sum, the DeepESN is a trustworthy and good method to predict rainfall; it could be applied to global climate forecasts which need high-volume data processing.
In ultrasonic equipment (anemometers and thermometers), for the measurement of parameters of atmospheric turbulence, a standard algorithm that calculates parameters from temporary structural functions constructed on the registered data is usually used. The algorithm is based on the Kolmogorov–Obukhov law. The experience of using ultrasonic meters shows that such an approach can lead to significant errors. Therefore, an improved algorithm for calculating the parameters is developed, which allows more accurate estimation of the structural characteristics of turbulent fluctuations, with an error that is not more than 10%. The algorithm was used in the development of a new ultrasonic hardware-software complex, autonomous meteorological complex AMK-03-4, which differs from similar measuring instruments of turbulent atmosphere parameters by the presence of four identical ultrasonic anemometers. The design of the complex allows not only registration of the characteristics of turbulence, but also measurement of the statistical characteristics of the spatial derivatives of turbulent temperature fluctuations and orthogonal components of wind speed along each of the axes of the Cartesian coordinate system. This makes it possible to investigate the space–time structure of turbulent meteorological fields of the surface layer of the atmosphere for subsequent applications in the Monin–Obukhov similarity theory and to study turbulent coherent structures. The new measurement data of the spatial derivatives of temperature at stable stratification (at positive Monin–Obukhov parameters) were obtained, at which the behavior of the derivatives was been investigated earlier. In the most part of the interval of positive Monin–Obukhov parameters, the vertical derivative of the temperature is close to a constant value. This fact can be considered as a new significant result in similarity theory.
Many older people with frailty are at risk of malnutrition and poor health, yet there is evidence that improving nutrition and weight loss can reduce frailty. This will become more important as the number of older people with frailty increases worldwide in future. Identifying those at risk is challenging due to the difficulty of reaching and screening those older people most at risk, the large number of nutritional assessment tools used, and the lack of consensus on the criteria to make a diagnosis of malnutrition. The management of older people with or at risk of malnutrition should be multi-modal and multi-disciplinary, and all care staff have an important role in delivering appropriate nutritional advice and support. This paper will highlight a number of practical approaches that clinicians can take to manage malnutrition in older people with frailty in community and acute settings, including environmental changes to enhance mealtime experience, food fortification and supplementation.
The present review retraces the steps of the industrial and agriculture revolution that have taken place up to the present day, giving ideas and considerations for the future. This paper analyses the specific challenges facing agriculture along the farming supply chain to permit the operative implementation of Industry 4.0 guidelines. The subsequent scientific value is an investigation of how Industry 4.0 approaches can be improved and be pertinent to the agricultural sector. However, industry is progressing at a much faster rate than agriculture. In fact, already today experts talk about Industry 5.0. On the other hand, the 4.0 revolution in agriculture is still limited to a few innovative firms. For this reason, this work deals with how technological development affects different sectors (industry and agriculture) in different ways. In this innovative background, despite the advantages of industry or agriculture 4.0 for large enterprises, small- and medium-sized enterprises (SMEs) often face complications in such innovative processes due to the continuous development in innovations and technologies. Policy makers should propose strategies, calls for proposals with aim of supporting SMEs to invest on these technologies and making them more competitive in the marketplace.
In recent years, intelligent sensor techniques have achieved significant attention in agriculture. It is applied in agriculture to plan the several activities and missions properly by utilising limited resources with minor human interference. Currently, plant cultivation using new agriculture methods is very popular among the growers. However, the aeroponics is one of the methods of modern agriculture, which is commonly practiced around the world. In the system, plant cultivates under complete control conditions in the growth chamber by providing a small mist of the nutrient solution in replacement of the soil. The nutrient mist is ejected through atomization nozzles on a periodical basis. During the plant cultivation, several steps including temperature, humidity, light intensity, water nutrient solution level, pH and EC value, CO 2 concentration, atomization time, and atomization interval time require proper attention for flourishing plant growth. Therefore, the object of this review study was to provide significant knowledge about early fault detection and diagnosis in aeroponics using intelligent techniques (wireless sensors). So, the farmer could monitor several paraments without using laboratory instruments, and the farmer could control the entire system remotely. Moreover, the technique also provides a wide range of information which could be essential for plant researchers and provides a greater understanding of how the key parameters of aeroponics correlate with plant growth in the system. It offers full control of the system, not by constant manual attention from the operator but to a large extent by wireless sensors. Furthermore, the adoption of the intelligent techniques in the aeroponic system could reduce the concept of the usefulness of the system due to complicated manually monitoring and controlling process.
One of the major problems of Indian economy is the uncertainty of agricultural production since agriculture is still a gamble in the monsoons. Since independence, there has been a considerable increase in the production of the agricultural sector and after the wave of green revolution wheat production touched heights. In spite of the spectacular growth in agricultural production, food grains output has been fluctuating from year after year. Green revolution no doubt increased food production but the main question which is still open is will it curb instability or give a push to it. Many economists tried to answer it like Mehra (1981), Hazell (1982), Mahendra Dev (1987), Larson & Ray (2004), and yet the conclusion were not convincing, so, there is still a big research gap on this issue. It is widely acknowledged that the growth rate in food grain output in India seems to have improved because of the impact of new technology. However, the impact of new technology on instability (year-to-year variations in food grain output) has not been quite clear and has remained a matter of concern. During a decade since the adoption of new technology (green revolution) the standard deviation and coefficient of variation of production of all the crop aggregates increased as compared with the pre-green revolution period. Instability of output of agriculture also results in causing instability in the related sectors. For example, a fall in the production of jute causes a small availability of raw material for the textile industry. Likewise, a cut-back in agricultural production also results in the reduction of employment in agriculture and this, in turn, reduces aggregate demand in the economy as the purchasing power of the cultivator falls. Thus, a major development issue for the Indian economy is to devise a strategy of agricultural development which can reduce instability and promise a steady growth of agricultural output (Dutt & Sundharam, 2006).
Capital formation is very crucial for economic growth (Classicals) which is to say that higher savings and investments in an economy will promote growth in all the sectors. Economists have emphasized on the role of capital formation in promoting agricultural growth rate and reducing poverty by arguing that with more capital in place, policy objectives of government for reducing poverty and ensuring food security can be met by stimulating public investment in the concerned sectors. This, in turn, would not only help in achieving stated policy targets but would also indirectly fuel growth for the agricultural sector in the economy. But, it does not mean that we have to use capital unnecessarily. It is of very vital importance that the pattern of investment should be so designed that agriculture should not be neglected in the policymaking. Certain areas such as defense equipment, heavy and engineering industries etc. should be permitted to use capital-intensive technology but as far as agriculture goods are concerned we must emphasize upon labour-absorbing technologies with judicious doses of capital. The objective of expansion of agriculture production and productivity in harmony with the fulfillment of full employment level of the economy is a necessary as well as sufficient condition for the overall development of Indian economy.
The application of sensors and information and communication technology (ICT) in agriculture has played a vital role in improving agricultural production and the value chain. Recently, the use of data analytics has shifted agriculture from input-intensive to knowledge-intensive as a large amount of agricultural data can be stored, shared, and analyzed to create information. In this paper, we have reviewed existing sensors and data analytics techniques used in different areas of agriculture. We have classified agriculture into five categories and reviewed the state-of-the-art technology in practice and ongoing research in each of these areas. Also, we have presented a case study of Korean scenario compared with other developed nations and addressed some of the issues associated with it. Finally, we have discussed current and future challenges and provided our views on how such issues can be addressed.
Low-frequency vibration is a harmful factor that affects the accuracy of micro/nano-measuring machines. Low-frequency vibration cannot be completely eliminated by passive control methods, such as the use of air-floating platforms. Therefore, low-frequency vibrations must be measured before being actively suppressed. In this study, the design of a low-cost high-sensitivity optical accelerometer is proposed. This optical accelerometer mainly comprises three components: a seismic mass, a leaf spring, and a sensing component based on a four-quadrant photodetector (QPD). When a vibration is detected, the seismic mass moves up and down due to the effect of inertia, and the leaf spring exhibits a corresponding elastic deformation, which is amplified by using an optical lever and measured by the QPD. Then, the acceleration can be calculated. The resonant frequencies and elastic coefficients of various seismic structures are simulated to attain the optimal detection of low-frequency, low-amplitude vibration. The accelerometer is calibrated using a homemade vibration calibration system, and the calibration experimental results demonstrate that the sensitivity of the optical accelerometer is 1.74 V (m·s⁻²)⁻¹, the measurement range of the accelerometer is 0.003–7.29 m·s⁻², and the operating frequencies range of 0.4–12 Hz. The standard deviation from ten measurements is under 7.9 × 10⁻⁴ m·s⁻². The efficacy of the optical accelerometer in measuring low-frequency, low-amplitude dynamic responses is verified.
The purposes of the research are to design and develop the automation control plant monitoring system and evaluate the effect of using this prototype. This system is able to control significant environmental factors that affect plant growth including temperature, humidity, light and water. To facilitate this process, the Arduino board was adapted to program the various sensors used to control temperature and humidity for cultivation. The applications of this prototype include the preparation of the plant nutrient solution, to design the automated system to control the opening—the leading solution for automated control system. The result of the experimental project demonstrated that the system works well in the automated mode.
• Soil thermal properties are required in environmental, Earth and planetary science, and engineering applications
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• The heat pulse method is a transient method that can be used for measuring estimating soil thermal properties, a variety of other physical and hydraulic parameters
• The development history of the heat pulse method over the past 130 years is summarized, the probe design, construction, calibration and applications of the heat pulse method in unfrozen and frozen soils are presented, and limitations and perspectives of the technique are discussed
Precision agriculture can be defined as the science of using technology to improve the agricultural production. It is advisable for farmers to use a decision support system; in fact, real–time monitoring of climatic conditions is the only way to know the water needed by a cultivation. On the other hand, since the 1990s, a strong decrease of the Mediterranean Quercus has been observed in the pastures of southwestern Spain and Portugal, causing a high mortality of holm and cork oak trees. Among the factors associated with this decrease, the radical decomposition caused by Phytophthora Cinnamomi is remarkable for its gravity, which makes it necessary to reforest the trees and to monitor the microclimatic factors that have an influence on this regeneration. Wireless Sensor Networks (WSN) are a technology in full evolution and development, as well as their appropriate use in cultivations that help to implement ecological techniques. With these considerations in this work five units/nodes with one or more sensors that allow different environmental readings have been developed. In this work, the acquisition of data obtained from different sensors has been achieved, allowing the monitoring of climatic elements such as soil moisture, air quality, temperature and humidity, rainfall intensity, precipitation level, wind speed and direction, luminous flux and atmospheric pressure. A web page has been designed where the user can consult the climatic conditions of the cultivation or reforestation. Different devices interconnected with a central unit have been developed where measurements of the cultivation are sent for its later analysis by the farmer. The microclimatic data acquisition developed in the WSN proposed in this paper allows a farmer to make decisions about the irrigation of the cultivation, use of fertilizers, the development and maturation phases of the cultivated products, obtaining the optimum stages of cultivation and harvesting.
We suggest a concept design of a SAW-based microaccelerometer with an original triangular-shaped console-type sensing element. Our design is particularly optimized to increase the robustness against positioning errors of the SAW resonators on the opposite sides of the console. We also describe the results of computer simulations and laboratory tests that are in a perfect agreement with each other and present the sensitivity characteristics of a manufactured experimental design device.
A more accurate and timely rainfall prediction is needed for flood disaster reduction and prevention in Wuhan. The in situ microelectromechanical systems’ (MEMS) sensors can provide high time and spatial resolution of weather parameter measurement, but they suffer from stochastic measurement error. In order to apply MEMS sensors in real-time rainfall prediction in Wuhan, firstly, seasonal trend decomposition using Loess (STL) algorithm is utilized to decompose the observed time series into trend, seasonal, and remainder components. The trend of the observed series is compared with the corresponding trend of the data downloaded from the authoritative website with the same weather parameter in terms of Euclidean distance and cosine similarity. The similarity demonstrates that the observation of MEMS sensors is believable. Secondly, the long short-term memory (LSTM) is used to predict the real-time rainfall based on the observed data. Compared with autoregressive and moving average (ARMA), random forest (RF), support vector machine (SVM), and back propagation neural networks (BPNNs), LSTM not only performs as well as ARMA in real-time rainfall prediction but also outperforms the other four models in seasonal rainfall pattern description and seasonal real-time rainfall prediction. Our experiment results show that more detailed, timely, and accurate rainfall prediction can be achieved by using LSTM on the MEMS weather sensors.
To meet the demand of intelligent irrigation for accurate moisture sensing in the soil vertical profile, a soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of five groups of sensing probes, a data processor, and some accessory components. Low-resistivity copper rings were used as components of the sensing probes. Composable simulation of the sensor’s sensing probes was carried out using a high-frequency structure simulator. According to the effective radiation range of electric field intensity, width and spacing of copper ring were set to 30 mm and 40 mm, respectively. A parallel resonance circuit of voltage-controlled oscillator and high-frequency inductance-capacitance (LC) was designed for signal frequency division and conditioning. A data processor was used to process moisture-related frequency signals for soil profile moisture sensing. The sensor was able to detect real-time soil moisture at the depths of 20, 30, and 50 cm and conduct online inversion of moisture in the soil layer between 0–100 cm. According to the calibration results, the degree of fitting (R²) between the sensor’s measuring frequency and the volumetric moisture content of soil sample was 0.99 and the relative error of the sensor consistency test was 0–1.17%. Field tests in different loam soils showed that measured soil moisture from our sensor reproduced the observed soil moisture dynamic well, with an R² of 0.96 and a root mean square error of 0.04. In a sensor accuracy test, the R² between the measured value of the proposed sensor and that of the Diviner2000 portable soil moisture monitoring system was higher than 0.85, with a relative error smaller than 5%. The R² between measured values and inversed soil moisture values for other soil layers were consistently higher than 0.8. According to calibration test and field test, this sensor, which features low cost, good operability, and high integration, is qualified for precise agricultural irrigation with stable performance and high accuracy.
In spite of its central importance in evolution, plant architecture and crop improvement, stem development remains poorly understood relative to other plant organs. Here, we summarise current knowledge of stem ontogenesis and its regulation, including insights from new image analysis and biophysical approaches. The stem initiates in the rib zone (RZ) of the shoot apical meristem, under transcriptional control by DELLA and BLH proteins. Links have emerged between these regulators and cell proliferation, patterning and oriented growth in the RZ. During subsequent internode elongation, cell wall properties and mechanics have been analysed in detail, revealing pectin modification as a prominent control point. Recent work has also highlighted signalling to coordinate growth of stem tissues with different mechanical properties.
The presence of external elements is a major limitation of current hearing aids and cochlear implants, as they lead to discomfort and inconvenience. Totally implantable hearing devices have been proposed as a solution to mitigate these constraints, which has led to challenges in designing implantable sensors. This work presents a feasibility analysis of a MEMS piezoelectric accelerometer coupled to the ossicular chain as an alternative sensor. The main requirements of the sensor include small size, low internal noise, low power consumption, and large bandwidth. Different designs of MEMS piezoelectric accelerometers were modeled using Finite Element (FE) method, as well as optimized for high net charge sensitivity. The best design, a 2 × 2 mm2annular configuration with a 500 nm thick Aluminum Nitride (AlN) layer was selected for fabrication. The prototype was characterized, and its charge sensitivity and spectral acceleration noise were found to be with good agreement to the FE model predictions. Weak coupling between a middle ear FE model and the prototype was considered, resulting in equivalent input noise (EIN) lower than 60 dB sound pressure level between 600 Hz and 10 kHz. These results are an encouraging proof of concept for the development of MEMS piezoelectric accelerometers as implantable sensors for hearing devices.
One of measurement methods to investigate the condition of the subsurface is by using geoelectric method. This research uses wenner-Schlumberger arrays configuration geoelectrical method which is mapping resistivity that is commonly known as profiling (2D) in order to identify the lateral and vertical anomaly of material resistivity. 2D resistivity cross section is obtained from the result of data- processing on software Res2Dinv. The data were obtained along 70 m using Wenner-Schlumberger configuration with 5 m spaced electrode. The approximated value of resistivity obtained from the data processing ranged from 1000-1548 Ωm and with the iteration error 87.9%. Based on the geological map of Ujung Pandang sheet, the location of the research is an alluvium and coastal precipitation area with grain in forms of gravel, sand, clay, mud, and coral limestone. Thus, by observing and analyzing the variety of the resistivity cross-section from the inversion data, there are areas (a) showing resistivity values ranged from 0.1-0.2 Ωm which is estimated to be salt water intrusion based on the resistivity table of Earth materials, and region (b) which is a mixture of sand and clay material with the range of resistivity values between 1-1000 μm.
In this paper, a novel single-axis MEMS tilt sensor is presented. It contains a hexagonal proof mass, six micro-lever force amplifiers and three double-ended-tuning fork (DETF) resonant strain gauges. The proof mass is placed in the center with the micro-levers and the DETFs radially arrayed around. The variation of gravity acceleration applied on the proof mass will result in frequency shifts of the DETFs. Angular tilt can be got by analyzing the frequency outputs. The structural design of the tilt sensor is optimized by finite element simulation and the device is microfabricated using a silicon-on-insulator process, followed by open-loop and closed-loop characterizations. Results show that the scale factor of such sensor is at least 11.53 Hz/degree. Minimum Allan deviation of the DETF oscillator is 220 ppb (parts per billion) of the resonant frequency for an 5 s integration time. Resolution of the tilt sensor is 0.002 ∘ in the whole measurement range from 0 ∘ to 360 ∘ .
Foliar wetness plays an essential role in plant disease cycles and epidemic development yet no cost-effective leaf wetness sensors (LWSs) are available that could be deployed within large areas to better understand that role. Electronic LWSs comprise an artificial leaf and the electronic circuitry able to measure electrical impedance changes due to water film or drops on the leaf surface. We propose a simple, compact and low-cost electronic interface circuit (EIC) for artificial leaves based on capacitance changes. The circuit relies on the charge-transfer capacitive sensing method and it is implemented by a microcontroller unit (MCU), which offers computation and communication capabilities currently missing in commercial LWSs, This EIC can be used in custom and commercial artificial leaves hence suits studies that require a close emulation of particular plant leaves.
In this paper, capacitive-type humidity sensors were prepared by sequentially drop-coating the aqueous suspensions of zinc oxide (ZnO) nanopowders and polyvinyl pyrrolidone–reduced graphene oxide (PVP-RGO) nanocomposites onto interdigitated electrodes. Significant improvements in both sensitivity and linearity were achieved for the ZnO/PVP-RGO sensors compared with the PVP-RGO/ZnO, PVP-RGO, and ZnO counterparts. Moreover, the produced ZnO/PVP-RGO sensors exhibited rather small hysteresis, fast response-recovery time, and long-term stability. Based on morphological and structural analyses, it can be inferred that the excellent humidity sensing properties of the ZnO/PVP-RGO sensors may be attributed to the high surface-to-volume ratio of the multilayer structure and the supporting roles of the PVP-RGO nanocomposites. The results in this work hence provide adequate guidelines for designing high-performance humidity sensors that make use of the multilayer structure of semiconductor oxide materials and PVP-RGO nanocomposites.
A highly sensitive fiber-optic accelerometer based on detecting the power output of resonances from the core dip is demonstrated. The sensing probe comprises a compact structure, hereby a short section of specific core (with a significant core dip) fiber stub containing a straight fiber Bragg grating is spliced to another single-mode fiber via a core self-alignment process. The femtosecond laser side-illumination technique was utilized to ensure that the grating inscription region is precisely positioned and compact in size. Two well-defined core resonances were achieved in reflection: one originates from the core dip and the other originates from fiber core. The key point is that only one of these two reflective resonances exhibits a high sensitivity to fiber bend (and vibration), whereas the other is immune to it. For low frequency (<10 Hz) and weak vibration excitation (<0.3 m/s²) measurement, the proposed sensor shows a much higher resolution (1.7 × 10⁻³ m/s²) by simply monitoring the total power output of the high-order core mode reflection. Moreover, the sensor simultaneously provides an inherent power reference to eliminate unwanted power fluctuations from the light source and transmission lines, thus providing a means of evaluating weak seismic wave at low frequency.
Sustainable food production is one of the major challenges of the twenty-first century in the era of global environmental problems such as climate change, increasing population and natural resource degradation including soil degradation and biodiversity loss. Climate change is among the greatest threats to agricultural systems. Green Revolution though multiplied agricultural production several folds but at the huge environmental cost including climate change. It jeopardized the ecological integrity of agroecosystems by intensive use of fossil fuels, natural resources, agrochemicals and machinery. Moreover, it threatened the age-old traditional agricultural practices. Agriculture is one of the largest sectors that sustain livelihood to maximum number of people and contribute to climate change. Therefore, a climate-smart approach to sustainable food production is the need of hour. Traditional agriculture is getting increased attention worldwide in context of sustainable food production in changing climate. The present article advocates traditional agriculture as a climate-smart approach for the sustainable food production and also deliberates the correlation between climate change and agriculture.
In this paper, for the first time an InGaP (GaInP) photon counting X-ray photodiode has been developed and shown to be suitable for photon counting X-ray spectroscopy when coupled to a low-noise charge-sensitive preamplifier. The characterisation of two randomly selected 200 μm diameter and two randomly selected 400 μm diameter In0.5Ga0.5P p⁺-i-n⁺ mesa photodiodes is reported; the i-layer of the p⁺-i-n⁺ structure was 5 μm thick. At room temperature, and under illumination from an ⁵⁵Fe radioisotope X-ray source, X-ray spectra were accumulated; the best spectrometer energy resolution (FWHM) achieved at 5.9 keV was 900 eV for the 200 μm In0.5Ga0.5P diameter devices at reverse biases above 5 V. System noise analysis was also carried out and the different noise contributions were computed.
The paper describes the structure, operating principle and software of the ultrasonic velocity and air flow meter with the arrangement of sensors at the vertices of the tetrahedron. This design, with a minimum of sensors, makes it possible to measure the velocity components in three directions at once. The influence of dimensions of the construction on the metrological characteristics of the device is evaluated.
Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. Based on PA management, the same routine to a crop regardless of site environments can be avoided. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. This review outlines the recent applications of WSNs in agriculture research as well as classifies and compares various wireless communication protocols, the taxonomy of energy-efficient and energy harvesting techniques for WSNs that can be used in agricultural monitoring systems, and comparison between early research works on agriculture-based WSNs. The challenges and limitations of WSNs in the agricultural domain are explored, and several power reduction and agricultural management techniques for long-term monitoring are highlighted. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data.
Agriculture is one of the economic sectors that affect climate change contributing to greenhouse gas emissions directly and indirectly. There is a trend of agricultural greenhouse gas emissions reduction, but any practice in this direction should not affect negatively farm productivity and economics because this would limit its implementation, due to the high global food and feed demand and the competitive environment in this sector. Precision agriculture practices using high-tech equipment has the ability to reduce agricultural inputs by site-specific applications, as it better target inputs to spatial and temporal needs of the fields, which can result in lower greenhouse gas emissions. Precision agriculture can also have a positive impact on farm productivity and economics, as it provides higher or equal yields with lower production cost than conventional practices. In this work, precision agriculture technologies that have the potential to mitigate greenhouse gas emissions are presented providing a short description of the technology and the impacts that have been reported in literature on greenhouse gases reduction and the associated impacts on farm productivity and economics. The technologies presented span all agricultural practices, including variable rate sowing/planting, fertilizing, spraying, weeding and irrigation.
Sap-flow (SF) measurements provide unique and valuable data for studying plant water relations and crop water use. In this study we utilize new developments in heat pulse theory, low-cost electronics, and 3D-printing to fabricate, calibrate, and field test an affordable research-grade sap flow instrument. Each gauge included three needle probes that were inserted into the stem. A central needle contained a resistance heater for applying the heat pulse, while two additional needles measured the resulting temperature increases at positions downstream and to the side of the heater. Time series data following a heat pulse were used to calculate heat velocity using two techniques. The T max method used the time to temperature maximum while a novel temperature ratio method (T m Ratio) used the ratio of the temperature maxima at the downstream and side probes. Data acquisition systems were built from low-cost Arduino microcontrollers. Prototype SF gauges were tested and calibrated for corn and sunflower in the greenhouse. Once calibrated for a specific gauge design and species, the gauges tracked gravimetric measurements of transpiration rate to within 10%. The T max method performed well under high rates of sap flow (i.e., up to 300 g hr −1) in both sunflower and corn, but overestimated flow at low transpiration rates. The new T m Ratio method accurately tracked sap flow at rates near 150 g hr −1 and also performed well during nighttime flows as low as 3 g hr −1 in corn. However, both theory and observation suggest the T m Ratio approach may fail at very high flow rates. The gauges and data acquisition systems were deployed in the field on irrigated corn. Sap flow was calculated using the T max method, the T m Ratio method, and a hybrid approach that used the T m Ratio method for flow rates < 130 g h −1 and the T max method for flow rates > 130 g h −1. Estimates of canopy transpiration over a two-week period were, on average, within 5% of calculated reference crop evapotranspiration. The do-it-yourself simplicity and low cost of the approach make it possible to deploy large numbers of gauges in the field to capture spatial variability, compare water use among agronomic plots, or scale-up sap flow to measure canopy transpiration.
Background
Malnutrition, low muscle strength and muscle mass are highly prevalent in older hospitalized patients and associated with adverse outcomes. Malnutrition may be a risk factor for developing low muscle mass. We aimed to investigate the association between the risk of malnutrition and 1) muscle strength and muscle mass at admission and 2) the change of muscle strength and muscle mass during hospitalization in older patients.
Methods
The EMPOWER study included 378 patients aged seventy years or older who were acutely or electively admitted to four different wards of an academic teaching hospital in Amsterdam. Patients were grouped into low risk of malnutrition and high risk of malnutrition based on the Short Nutritional Assessment Questionnaire (SNAQ) score and were assessed for hand grip strength and muscle mass using hand held dynamometry respectively bioelectrical impedance analysis (BIA) within 48 h after admission and at day seven, or earlier at the day of discharge. Muscle mass was expressed as skeletal muscle mass, appendicular lean mass, fat free mass and the skeletal muscle index.
Results
The mean age of the patients was 79.7 years (SD 6.39), 48.9% were female. At admission, being at high risk of malnutrition was significantly associated with lower muscle mass (Odds Ratio, 95% CI, 0.90, 0.85–0.96), but not with muscle strength. Muscle strength and muscle mass did not change significantly during hospitalization in both groups.
Conclusion
In older hospitalized patients, a high risk of malnutrition is associated with lower muscle mass at admission, but not with muscle strength nor with change of either muscle strength or muscle mass during hospitalization.
Regression problems provide some of the most challenging research opportunities in the area of machine learning, and more broadly intelligent systems, where the predictions of some target variables are critical to a specific application. Rainfall is a prime example, as it exhibits unique characteristics of high volatility and chaotic patterns that do not exist in other time series data. This work’s main impact is to show the benefit machine learning algorithms, and more broadly intelligent systems have over the current state-of-the-art techniques for rainfall prediction within rainfall derivatives. We apply and compare the predictive performance of the current state-of-the-art (Markov chain extended with rainfall prediction) and six other popular machine learning algorithms, namely: Genetic Programming, Support Vector Regression, Radial Basis Neural Networks, M5 Rules, M5 Model trees, and k-Nearest Neighbours. To assist in the extensive evaluation, we run tests using the rainfall time series across data sets for 42 cities, with very diverse climatic features. This thorough examination shows that the machine learning methods are able to outperform the current state-of-the-art. Another contribution of this work is to detect correlations between different climates and predictive accuracy. Thus, these results show the positive effect that machine learning-based intelligent systems have for predicting rainfall based on predictive accuracy and with minimal correlations existing across climates.
Resistivity-type humidity sensors have been investigated with great interest due to the increasing demands in industry, agriculture and daily life. To date, most of the available humidity sensors have been fabricated based on negative humidity impedance, in which the electrical resistance decreases as the humidity increases, and only several carbon composites have been reported to present positive humidity impedance. However, here we fabricate positive impedance humidity sensors only via single-component WO3−x crystals. The resistance of WO3−x crystal sensors in response to relative humidity could be tuned from a negative to positive one by increasing the compositional x. And it was revealed that the positive humidity impedance was driven by the defects of oxygen vacancy. This result will extend the application field of humidity sensors, because the positive humidity impedance sensors would be more energy-efficient, easier to be miniaturized and electrically safer than their negative counterparts for their lower operation voltages. And we believe that constructing vacancies in semiconducting materials is a universal way to fabricate positive impedance humidity sensors.
Efficiency increase of resources through automation of agriculture requires more information about the production process, as well as process and machinery status. Sensors are necessary for monitoring the status and condition of production by recognizing the surrounding structures such as objects, field structures, natural or artificial markers, and obstacles. Currently, three dimensional (3-D) sensors are economically affordable and technologically advanced to a great extent, so a breakthrough is already possible if enough research projects are commercialized. The aim of this review paper is to investigate the state-of-the-art of 3-D vision systems in agriculture, and the role and value that only 3-D data can have to provide information about environmental structures based on the recent progress in optical 3-D sensors. The structure of this research consists of an overview of the different optical 3-D vision techniques, based on the basic principles. Afterwards, their application in agriculture are reviewed. The main focus lays on vehicle navigation, and crop and animal husbandry. The depth dimension brought by 3-D sensors provides key information that greatly facilitates the implementation of automation and robotics in agriculture.
Sustainability of irrigation practices is an important objective to be pursued in many countries, especially in regions where water scarcity causes strong conflicts among different water uses. The efficient use of irrigation water is a key factor in coping with the food demands of an increasing world population and with the negative effects of climate change on water resource availability in many areas. In this complex context, it is important that farmers can rely on instruments and practices that enable a better management of water at the field scale, whatever the irrigation method they adopt. In this paper, we present an open-hardware device based on the Arduino technology that was developed to allow the continuous monitoring of soil water potential in the root zone for supporting irrigation scheduling at the field scale. The structure of the device is flexible and can be adapted to host different types of sensors. The results, obtained managing irrigation in a peach orchard, show that adoption of the device, together with a simple irrigation scheduling criterion, allowed a significant increase of water use efficiency without causing a reduction of the quantity and quality of crop production.
In plant factories, plants are usually cultivated in nutrient solution under a controllable environment. Plant quality and growth are closely monitored and precisely controlled. For plant growth evaluation, plant weight is an important and commonly used indicator. Traditional plant weight measurements are destructive and laborious. In order to measure and record the plant weight during plant growth, an automated measurement system was designed and developed herein. The weight measurement system comprises a weight measurement device and an imaging system. The weight measurement device consists of a top disk, a bottom disk, a plant holder and a load cell. The load cell with a resolution of 0.1 g converts the plant weight on the plant holder disk to an analog electrical signal for a precise measurement. The top disk and bottom disk are designed to be durable for different plant sizes, so plant weight can be measured continuously throughout the whole growth period, without hindering plant growth. The results show that plant weights measured by the weight measurement device are highly correlated with the weights estimated by the stereo-vision imaging system; hence, plant weight can be measured by either method. The weight growth of selected vegetables growing in the National Taiwan University plant factory were monitored and measured using our automated plant growth weight measurement system. The experimental results demonstrate the functionality, stability and durability of this system. The information gathered by this weight system can be valuable and beneficial for hydroponic plants monitoring research and agricultural research applications.
This paper reports the study of an organic semiconductor nickel phthalocyanine (NiPc) based thin film surface-type integrated capacitive and resistive type humidity sensor. The capacitance and resistance of the fabricated devices were evaluated at room temperature in the relative humidity (RH) range of 35-95% and 35-75% RH, respectively. In general, an increase in capacitance and decrease in resistance of the Ag/NiPc/Ag sensor was observed with the rise in humidity level. Humidity dependent capacitance and resistance properties of this sensor make it attractive for use in humidity sensors. The response and recovery characteristic of the humidity sensor were also investigated.
In this study, UV-visible flexible resistivity-type photo-detectors were demonstrated with CdS-nanowires (NWs) percolation network channel and Ag-NWs percolation network electrode. The devices were fabricated on Mixed Cellulose Esters (MCE) membrane using a lithographic filtration method combined with a facile non-transfer process. The photo-detectors demonstrated strong adhesion, fast response time, fast decay time, and high photo sensitivity. The high performance could be attributed to the high quality single crystalline CdS-NWs, encapsulation of NWs in MCE matrix and excellent interconnection of the NWs. Furthermore, the sensing performance was maintained even the device was bent at an angle of 90°. This research may pave the way for the facile fabrication of flexible photo-detectors with high performances.
Precision agriculture technologies (PATs) offer an approach to arable systems which both enhance productivity and minimise environmental harm. Despite expected economic gains uptake by farmers has been low. This paper explores the intended adoption of PATs through a survey of 971 farmers growing wheat, potato and cotton in five European countries. We apply a count data modelling framework to accommodate the inherent structural differences between the current adopters and non-adopters of PATs. This is augmented by qualitative analysis of the main thematic reasons for intended uptake. Results indicate non-adopters have more belief in their knowledge of field topology and are generally older than current adopters. Those non-adopters intending to adopt PATs in the future are more favourable to a wider range of incentives than current adopters. Attitudinal differences towards the economic certainty of investment and payback periods also emerge. The results indicate that a gradient of adoption is occurring within European arable farming systems which may lead to inequalities in technology access. Recognition of these differences at policy level could lead to cost-effective interventions which maximise uptake, generate returns to candidate farmers and meet policy desires for sustainable agricultural production in the future.
According to their inherent characteristics, MEMS resonant accelerometers are suitable for low-frequency, low-g acceleration measurement. In this paper, we report a MEMS accelerometer based on double-ended-tuning fork resonators. The scale factor of our sensor is 1153.3 Hz/g and the bias stability of the oscillator is 58 ppb (parts per billion) for an averaging time of 1 s. The static test showed that the resolution of our sensor was 13.8 μg. The dynamic performance was demonstrated by single-frequency and hybrid-frequency vibration tests, and the results showed that our device is suitable for detecting low-frequency vibration (0.5–5 Hz). The cross-axis sensitivity is 1.33%. Compared with a standard charge accelerometer, our device showed its superiority in mixed acceleration measurement, which makes it a potentially attractive option for geophone or seismometer applications.
This paper introduces a novel sensing element scheme to improve the accuracy of 2-D micromachined thermal wind sensor. The sensing element consists of eight heaters and eight thermistors placed on ceramic substrate. The octagon-arranged sixteen resistors are divided into two wind sensing groups, cross-type group and saltire-type group. These two groups monitor wind independently, and the final measured wind speed and direction are extracted from the measurement results of the two groups. Based on this octagon-sensing design, the sensor achieves higher accuracy for wind measurement, which is verified by the experiment. The results show that for the wind speed up to 33 m/s, the sensor has a measurement error less than ± 1%, and the airflow direction over the full range of 360° is determined with a maximum error of ± 1.5°. [2018-0045]
In this paper, we introduce Mago, a novel system that can infer a person's mode of transport (MOT) using the Hall-effect magnetic sensor and accelerometer present in most smart devices. When a vehicle is moving, the motions of its mechanical components such as the wheels, transmission and the differential distort the earth's magnetic field. The magnetic field is distorted corresponding to the vehicle structure (e.g., bike chain or car transmission system), which manifests itself as a strong signal for sensing a person's transportation modality. We utilize this magnetic signal combined with the accelerometer and design a robust algorithm for the MOT detection. In particular, our system extracts frame-based features from the sensor data and can run in nearly real-time with only a few seconds of delay. We evaluated Mago using over 70 hours of daily commute data from 7 participants and the leave-one-out analysis of our cross-user, cross-device model reports an average accuracy of 94.4% among seven classes (stationary, bus, bike, car, train, light rail and scooter). Besides MOT, our system is able to reliably differentiate the phone's in-car position at an average accuracy of 92.9%. We believe Mago could potentially benefit many contextually-aware applications that require MOT detection such as a digital personal assistant or a life coaching application.
This chapter is dedicated to Micro-electro-mechanical Systems (MEMS) devices developed for primary use in agriculture. We can see MEMS devices in ink jet printers (printer heads), automobiles (e.g., airbag accelerometer), projectors (digital micromirror device for DLP projectors), mobile devices (e.g., gyroscopes for smartphones, tablets, etc.), healthcare applications (e.g., lab-on-a-chip for the detection of multiple tropical infectious diseases), among others. Although it is expected to grow, the use of MEMS devices used in agriculture still comes down to a few cases in research centers. However, due to demand for improved agricultural processes and the future widespread use of the Internet of Things (IoT), a high demand for small size, low cost, low power, and easily mass produced devices is expected. This context suggests the use of MEMS devices for both sensing elements, and for energy harvesters. In this chapter, only the sensor elements whose major use is agriculture will be addressed. From this perspective, the main parameters used in agriculture will be addressed taking into account research and development ever held in MEMS devices for measuring these parameters. These key parameters are grouped into classes: environment, soil, agricultural crops, and livestock. For almost all of the parameters shown, MEMS devices showed encouraging results. Most work with MEMS for agriculture has been done in laboratories so far. However, transitioning to field applications seems feasible. Potential advantages of MEMS are: small size, economical production (specially in large scale), built-in electronics (for auto-calibration, self-testing, digital compensation, and digital communications), and low power consumption—ideal for the use in Precision Agriculture complemented by Internet of Things. In short, this chapter will allow researchers developing MEMS devices to have a knowledge of what has already been developed for agriculture and to have an idea of future needs in this field.
A Strategy for Assessing Science offers strategic advice on the perennial issue of assessing rates of progress in different scientific fields. It considers available knowledge about how science makes progress and examines a range of decision-making strategies for addressing key science policy concerns. These include avoiding undue conservatism that may arise from the influence of established disciplines; achieving rational, high-quality, accountable, and transparent decision processes; and establishing an appropriate balance of influence between scientific communities and agency science managers. A Strategy for Assessing Science identifies principles for setting priorities and specific recommendations for the context of behavioral and social research on aging. © 2007 by the National Academy of Sciences. All rights reserved.
This paper reports a novel micro airflow anemometer based on the hot-film sensing principle, i.e. gas cooling of electrically heated resistance. The sensor with three Ti/Pt hot-film sensing components with width of 50µm and thickness of 130nm has been fabricated on a glass tube in a UV lithography system with multi-layer alignment by our developed MEMS compatible lab-on-a-tube technology. The micro anemometer has demonstrated high sensitivity, fast response and ability to detect wind speed and direction. The influence of environment temperature on output voltage when micro anemometer with and without the temperature compensation was evaluated. And the test response time is in the range of milliseconds, also faster than several seconds reported for that in-plane one [1]. In the wind speed and direction field-test, using a commercial ultrasonic anemometer as the reference, achieved mean error of direction is 13.5° and mean error of velocity is 0.02m/s.
Undergraduate physics laboratories seldom have experiments that measure the Coriolisacceleration. This has traditionally been the case owing to the inherent complexities of making such measurements. Articles on the experimental determination of the Coriolisacceleration are few and far between in the physics literature. However, because modern smartphones come with a raft of built-in sensors, we have a unique opportunity to experimentally determine the Coriolisacceleration conveniently in a pedagogically enlightening environment at modest cost by using student-owned smartphones. Here we employ the gyroscope and accelerometer in a smartphone to verify the dependence of Coriolisacceleration on the angular velocity of a rotatingtrack and the speed of the sliding smartphone.