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UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review
Abstract
Water management is becoming a critical issue for sustainable agriculture, especially in the semi-arid region, where problems with water scarcity are rising. More accurate water status recovery in crops is required for precise irrigation through remote sensing technologies. These technologies have a lot of potential in intelligent irrigation because they allow for real-time environmental data collection. Nowadays, digital practices have been used, such as unmanned aerial vehicle (UAV), which plays an essential role in various applications related to crop management. Drones offer an exciting opportunity to track crop fields with high spatial and temporal resolution remote sensing to enhance water stress management in irrigation. Farmers have historically depended on soil moisture measurements and weather conditions to detect crop water status for irrigation scheduling. This review paper summarizes the use of UAV remote sensing data in crops for estimating the water status and gives a detailed summary of the potential capacity of UAV remote sensing for water stress application. The remote sensing techniques help modify agricultural practices to meet this significant challenge by providing repeated information on crop status at different scales and various performances during the season. UAVs successful implementation in water stress estimations depends on UAV features, such as flexibility of use in flight planning, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. UAV with a thermal sensor is considered the most effective technique for detecting water stress using specific indices. Thermal imaging can identify water status variations and crop water stress index (CWSI). This CWSI acquired through UAV thermal sensors imagery can be acceptable for managing real-time irrigation to achieve optimum crop water efficiency.
... Singh et al. (2022) conducted a scholarly investigation similar to Rejeb et al. (2022), focusing mainly on viticulture [31]. In contrast, Awais et al. (2022) detailed crop water status estimation using UAV-based methods [32]. Nhamo et al. (2020) examined the significance of UAVs in agricultural water management and crop health, highlighting their potential as an alternative approach to enhance productivity in smallholder farms [4]. ...
... The CWSI integrates canopy temperature and environmental variables such as humidity and solar radiation to comprehensively measure plant water stress. Awais et al. (2023) revealed that the CWSI obtained via thermal sensors on UAVs might be deemed suitable for real-time irrigation management. Additionally, this index has been used in the detection of ET, as shown by previous studies conducted by Bellvert Stomatal conductance (4%) and canopy temperature (3%) are also valuable indicators of water stress [5,42]. ...
... et al. (2014), Santesteban et al. (2017), and Awais et al. (2023)[25,32,41]. ...
While there is immense potential in using unmanned aerial vehicles (UAVs) to facilitate precision water management, there is currently no consensus on practical strategies to operationally implement these technologies to guide water resources management decisions, particularly within smallholder farming contexts. To address this gap, this study employs bibliometric techniques to assess the current state of UAV applications for evapotranspiration (ET) estimation in agricultural settings. The analysis of 49 peer-reviewed papers from Scopus was conducted using Biblioshiny and VOSviewer to enhance comprehension of this expanding research field. The study highlights a significant increase in scholarly research on utilising UAVs for precision water management over the past decade. The investigations indicate that UAVs in agriculture are gaining prominence and exhibit substantial potential for various precision agriculture (PA) applications. Significant cost reductions for UAV technology and RS are anticipated soon, primarily driven by the availability of open-source platforms for processing tasks, such as Google Earth Engine. This research aims to inform smallholder farmers about the benefits of integrating UAVs into their farming practices, enhancing operational efficiency and productivity. Policymakers can use these findings to develop regulatory frameworks and incentive schemes that facilitate UAV adoption among smallholder farmers. Additionally, technology developers can leverage insights from this study to identify areas needing innovation and optimisation tailored to small-scale agriculture. Hence, this study seeks to bridge the gap between technological advancements and practical agricultural applications, promoting sustainable farming practices and enhancing the socioeconomic welfare of smallholder farmers.
... For SSCM, monitoring spatial crop, soil and weather data is very crucial. This becomes feasible thanks to GNSS-based technologies, GPS mounted crop harvesters, crop and soil sensing systems and digital sensors (Long et al. 2016;Jin et al. 2019;Liu et al. 2021;Perez-Ruiz, Martínez-Guanter, and Upadhyaya 2021;Srikanth, Chakraborty, and Murthy 2021;Yin et al. 2021;Awais et al. 2022;Hallik et al. 2022). ...
... Many agricultural sensors and tools are available to assess spatial crop, soil and weather data (Kassim, Mat, and Harun 2014;Kumar and Ilango 2018;Srikanth, Chakraborty, and Murthy 2021;Yin et al. 2021;Hallik et al. 2022). These in situ and on-the-go sensors are: LIDAR and RADAR sensors (Esch et al. 2018); UAV drones (Inoue 2020;Awais et al. 2022); electromagnetic induction (EMI) sensor; airborne imageries (Tack et al. 2019); hyperspectral sensing systems (Jin et al. 2019) and field sensors (Svotwa et al. 2013). Automated soil sensors are used to capture various types of soil characteristics (Wall and King 2004). ...
... Drone-based remote sensing can be successfully applied to detect the crop water stress for irrigation planning, using thermal sensors (Awais et al. 2022). ...
Precision agriculture (PA) has great potential to increase agricultural productivity and profitability while reducing input costs and environmental impacts. Within PA, site-specific crop management (SSCM) is considered the main premise, in which tillage operations and precise crop inputs (such as seed, fertiliser, water, pesticide and agrochemical) are applied according to field variability. The main aim of this review was to highlight the methods and tools used for spatial crop monitoring, soil and weather data influencing crop productivity and to support the adoption of SSCM technology. To achieve this goal: we discussed the main five components of SSCM, methods for monitoring crop and soil data, delineating field management zones (FMZs) and variable rate technologies (VRT) such as precision planting and digital smart sensors used for SSCM application. The review summarised that recent advances in plant and soil sensing systems, artificial intelligence (AI) and machine learning should be used in retrieving and analysing GIS big data for optimised crop inputs supply. Within VRT, light-bar systems, automatic controllers and sensors are user-friendly technologies that should be employed in SSCM solution. The authors highlight that adoption of PA can be increased through proper training and education of the farmers, and developing simple, affordable and efficient PA technologies. The review suggests five criteria that should be strictly adopted to get maximum benefits from SSCM: (i) all factors influencing crop yields can be identified; (ii) their effects on crop yields can be determined by using appropriate digital tools and crop modelling; (iii) variable rate crop inputs (VRCIs) should be calculated based on accurate information obtained from plant, soil and environment; (iv) targeted crop inputs should be exercised through global positioning system (GPS) enabled automatic controllers or wireless sensors network (WSN); and (v) right doses of crop inputs (e.g., nitrogen and irrigation) must be applied at the right time and place.
... Thermal infrared imagery can help identify areas with water stress, aligning with I5.0's resilience ability in agriculture by allowing farmers to adapt irrigation practices accordingly. The work proposed in [126] involves the use of UAVs for precision agriculture applications such as tracking crop health, estimating nutrient status, yield, and crop water demand. Thermal sensors deployed in the UAVs were used to monitor the surface temperature of the crops before and after irrigation to identify plant water stress in crops. ...
... This indicates that the real-time process threats pertain to the challenges related to the accumulation, storage, and processing of large volumes of remote sensing data for agricultural monitoring and management. Threats related to technology: Traditional satellite remote sensing systems face challenges in meeting the real-time processing and intelligent service demands for satellite remote sensing imagery [126]. This includes challenges such as the inability to meet the massification and real-time application needs of satellite remote sensing imagery and the urgent need to develop intelligent satellite systems to resolve these issues. ...
Agriculture can be regarded as the backbone of human civilization. As technology evolved, the synergy between agriculture and remote sensing has brought about a paradigm shift, thereby entirely revolutionizing the traditional agricultural practices. Nevertheless, the adoption of remote sensing technologies in agriculture face various challenges in terms of limited spatial and temporal coverage, high cloud cover, low data quality and so on. Industry 5.0 marks a new era in the industrial revolution, where humans and machines collaborate closely, leveraging their distinct capabilities, thereby enhancing the decision making capabilities, sustainability and resilience. This paper provides a comprehensive survey on remote sensing technologies and related aspects in dealing with the various agricultural practices in the Industry 5.0 (I5.0) era. We also elaborately discuss the various applications pertaining to I5.0- enabled remote sensing for agriculture. Finally, we discuss several challenges and issues related to the integration of I5.0 technologies in agricultural remote sensing. This comprehensive survey on remote sensing for agriculture in Industry 5.0 era offers valuable insights into the current state, challenges, and potential advancements in the integration of remote sensing technologies and Industry 5.0 principles in agriculture, thus paving the way for future research, development, and implementation strategies in this domain.
... In most studies from the retrieved literature, there were strong correlations between the stomatal conductance and water relation status or leaf water content [28,66,67]. Specifically, 17% of studies based on drone remote sensing researched the NUS leaf water content. ...
... Specifically, 17% of studies based on drone remote sensing researched the NUS leaf water content. Water content is an important indicator of crop health [28,66,67]. A plant with higher water potential will produce greener pigmentation and have increased crop productivity. ...
Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South.
... By deploying sensors on various platforms-such as satellites, aircraft, drones, and ground stations-remote sensing allows for the efficient capture of spatial data essential to modern agriculture, enabling a broader, more accurate understanding of environmental dynamics (Quattrochi and Goodchild, 2023). In agriculture, remote sensing enables the monitoring of crops, soil health, and weather patterns, providing farmers with actionable insights for optimizing resource use, reducing waste, and improving productivity (Awais et al., 2022). This technology has become crucial as the global population continues to rise, necessitating sustainable and efficient farming practices to ensure food security (Javaid et al., 2022;Wijerathna and Pathirana, 2022). ...
Remote sensing technology has transformed agriculture by providing critical insights into crop management, resource allocation, and environmental sustainability. By utilizing satellite imagery, aerial photography, and sensors, it enables precise monitoring of crop health, disease detection, and nutrient deficiencies throughout the crop lifecycle. This early detection capability allows for targeted interventions, reducing crop losses and minimizing the need for chemical inputs. Remote sensing aids in optimal planting and harvesting, enhancing both yield and quality, while supporting sustainable land use through environmental monitoring and policy development. As agriculture faces global challenges such as population growth, climate change, and resource constraints, remote sensing, combined with GIS and GPS technologies, plays a pivotal role in ensuring food security and environmental sustainability. It enables comprehensive assessment of soil and climate variability, contributing to improved agricultural outputs and resource efficiency. In India, where modern agricultural practices are increasingly adopted, remote sensing is integral to nutrient, water, and pest management, soil mapping, and crop monitoring. This technology is indispensable for addressing biotic and abiotic stressors and promotes smart farming practices that align with sustainable agricultural goals. By continuing to integrate remote sensing into agricultural processes, it holds great promise for improving food security and ensuring environmentally friendly farming for future generations.
Keywords: Remote sensing, Agriculture, Crop monitoring, Sustainability, Nutrient management
... Water scarcity is a persistent issue in many regions, efficient irrigation management is vital. Drones equipped with thermal cameras and Golden Leaf Publishers Precision Agriculture Advances ISBN: 978-81-19906-34-5 51 multispectral sensors can monitor soil moisture levels across large fields, providing real-time data that helps farmers make informed irrigation decisions (Awais et al., 2022). For example, in the arid regions of Rajasthan and Gujarat, drones have been used to map soil moisture variations, allowing for precise irrigation scheduling. ...
... This approach enabled accurate estimation of crop yield potential before harvest, facilitating informed decision-making for crop management practices [153]. Awais et al. [154] stated drones equipped with thermal and multispectral sensors were employed to monitor plant emergence dynamics in soybean fields. The study focused on early-season monitoring of seedling emergence rates and uniformity across large agricultural plots. ...
The global population is rapidly increasing, so there is a critical requirement to satisfy the food production demand. Conventional methods of agriculture are inadequate to meet building demand which leads to declining farming sector and adaptable to other industries. Most of the farming activities are highly dependent on the labor which leads to increase in cost and time of operation. The rapid growth of mechanization for all farm activities cannot completely reduce the human involvement. As a result, agricultural automation is critically important. In terms of automation, this study emphasizes the crucial role of UAVs in precision and smart agriculture. The adoption of drones for various farm operations has the possibility to minimize labor requirements as well as operational time. This review provides overview of conceptual design, command flow operation, Micro-controller boards, remote-control systems and attachments like sensors, cameras, motors in UAVs for the purpose of automation in farm activities. The Internet of Things (IoT) employed in UAVs with image processing and machine learning algorithms provides accurate and precision results in farm activities. Furthermore, this study discusses future advancements, limitations and challenges for farmers in adapting to UAVs.
Graphical Abstract
... It involves the collection and interpretation of data from a distance, often using satellites, aircraft, or drones equipped with sensors capable of detecting different wavelengths of light. One of the key benefits of remote sensing for detecting plant stress is its ability to capture large-scale, spatially explicit information about plant health and vigor [13]. By measuring the spectral reflectance of plants across different wavelengths of light, remote sensing can provide valuable insights into the physiological and biochemical changes that occur in plants under stress. ...
In this study, the emphasis is on assessing how satellite-derived vegetation indices respond to drought stress characterized by meteorological observations. This study aimed to understand the dynamics of grassland vegetation and assess the impact of drought in the Wielkopolskie (PL41) and Podlaskie (PL84) regions of Poland. Spatial and temporal characteristics of grassland dynamics regarding drought occurrences from 2020 to 2023 were examined. Pearson correlation coefficients with standard errors were used to analyze vegetation indices, including NDVI, NDII, NDWI, and NDDI, in response to drought, characterized by the meteorological parameter the Hydrothermal Coefficient of Selyaninov (HTC), along with ground-based soil moisture measurements (SM). Among the vegetation indices studied, NDDI showed the strongest correlations with HTC at r = −0.75, R² = 0.56, RMSE = 1.58, and SM at r = −0.82, R² = 0.67, and RMSE = 16.33. The results indicated drought severity in 2023 within grassland fields in Wielkopolskie. Spatial–temporal analysis of NDDI revealed that approximately 50% of fields were at risk of drought during the initial decades of the growing season in 2023. Drought conditions intensified, notably in western Poland, while grasslands in northeastern Poland showed resilience to drought. These findings provide valuable insights for individual farmers through web and mobile applications, assisting in the development of strategies to mitigate the adverse effects of drought on grasslands and thereby reduce associated losses.
... Another team at the University of Bonn is working on getting the robot to recognize its location. For this, a drone surveys the field and sends a map to the robot that allows it to navigate to accuracy of 2 cm [108]. Currently, no other machine is capable of greater accuracy. ...
The purpose of agriculture is to support humankind. There are currently 7.7 billion people on the planet and this figure will increase to nine billion by 2050. As the population grows, even greater amounts of food will be needed, creating a significant challenge for farmers. Emerging digital technologies such as hyper-automation have the potential to revolutionize conventional agricultural methods. This study assessed the current use of hyper-automation systems in agriculture and examined whether new uses of this technology could benefit agricultural industries. One example could be to use an automated variable-seed control system, which has reported seeding accuracy of 98 %, indicating a cost-effective solution. Overall, our analysis revealed that to sustain future agricultural production and ensure food security, countries throughout the world need to focus on hyper-automation in the agriculture sector.
... The results shown in Table A57 below indicate average premia of ¥5.70 for a Chinese organic-labelled carton of milk (approximately 173 per cent of the retail price of an equivalent non-organic product), ¥10.25 for the Chinese and EU organic-labelled carton of milk (approximately 331 per cent of the retail price of an equivalent non-organic product), and ¥11.56 for the Chinese, EU and US organic-labelled carton of milk (approximately 350 per cent of the retail price of an equivalent non-organic product). However, negative WTP values were associated with the combination of any number of organic labels and the PEOP label, suggesting a possible negative accumulation effect when placed in combination on milk carton packaging (Zhu et al., 2023). Chinese and EU organic labels + PEOP label -¥3.43 [-1.40, -5.52] Chinese, EU and US organic labels + PEOP label -¥3.88 [-2.15, -5.70] ...
this report presents an academic literature review of the latest research relevant to the international and domestic drivers of land use change and/or practice. The initial literature review undertaken in the first Drivers Project identified a preliminary list of 30 drivers (Saunders et al., 2016). This was updated in the years 2018, 2019 and 2022 to include new arising issues or drivers relevant to land use change/practice (Saunders et al., 2018; Driver et al., 2019; 2022). The current list of international and domestic drivers is presented in Table 1-1 below. This report has expanded upon previous literature reviews, with an examination of the latest reports produced by key organisations such as the United Nations (including the FAO and IPCC), as well as key academic literature. A summary of each driver and its impact on land use change and/or practice (where possible) has been compiled.
... Another team at the University of Bonn is working on getting the robot to recognize its location. For this, a drone surveys the field and sends a map to the robot that allows it to navigate to accuracy of 2 cm [108]. Currently, no other machine is capable of greater accuracy. ...
... Remote sensing (RS) data from a variety of sensors has become widely available, making it possible to predict crop yields at different scales (Liu et al., 2020b). For example, moderate-resolution imaging spectroradiometer (MODIS) data has been used for different applications such as crop health monitoring and disease detection (Awais et al., 2022;Ren et al., 2008). Vegetation indices (VIs) including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) derived from MODIS data have been utilized to evaluate crop health (Kouadio et al., 2014). ...
... Awais et al. [4] compared the forcing method with the updating method and found that when the observed values were assimilation in weekly steps, the updating method could improve the error by 65%, while the forcing method could only achieve 20%. However, from the perspective of technology and operation, it was still difficult to have available LAI observation results for the whole growing season [5]. ...
Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R2 of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.
... In this theory, [10] the semi-arid region, where issues with water scarcity are escalating, water management is more crucial for sustainable farming. In order to accurately irrigate crops using remote sensing technology, more precise water status recovery in crops is needed. ...
... They measure the radiation released by items such as crops and soil, which is connected to their temperature. Based on temperature fluctuations, thermal sensing can be used to analyze water stress, identify irrigation efficiency, and monitor crop health (Awais et al. 2022). ...
Remote sensing, especially in the analysis of crop canopies, has provided valuable agronomic insights. It plays a crucial role in crop classification, monitoring, and yield estimation. By continually offering data on the health of crops during the growing season at various measures and for multiple stakeholders, remote sensing has the potential to support the changing advancement of farming techniques in order to meet this significant challenge. We begin by providing a summary of the most recent remote sensing methods that are pertinent to the agricultural context. Remote sensing helps in addressing this variability by providing a comprehensive view of the agricultural landscape. Factors influencing agriculture, such as soil conditions and climate, can vary significantly in both space and time. Remote sensing allows for a comprehensive assessment of these variations. Remote sensing provides a consistent and reliable method for this purpose. Remote sensing works in conjunction with other cutting-edge techniques like GIS (Geographic Information Systems) and GPS (Global Positioning System) to enhance the evaluation and management of agricultural activity. Over the last 20 years, there has been a notable increase in the use of remote sensing technologies to improve agricultural output. This trend has been noticed with a significant boost in agricultural production
... Additionally, accuracy in detecting and categorizing fruits relies heavily on high-quality data, requiring careful image capture and preprocessing [20]. Traditional methods using a single drone often fail to gather enough data, due to restrictions like battery life, carrying capacity, and limited viewing angles [21,22]. ...
In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and precise data necessary for these tasks. With the agricultural sector increasingly relying on technological advancements, the integration of innovative solutions is essential. This study presents a novel approach that combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs). The proposed approach demonstrates superior real-time capabilities in fruit detection and counting, utilizing a combination of AI techniques and multi-UAV systems. The core innovation of this approach is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and, ultimately, a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring the high-resolution and accurate detection of targeted objects during UAV operations. Its effectiveness is proven by experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate at 14.7% and a false negative rate at 18.3%, even under challenging weather conditions like cloudiness. Overall, the practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0.
... Most of the existing works on thermal imaging are employed to measure the water stress in the plant. Also, they can be utilized to recognize the stress caused by pests and diseases (Awais et al., 2022). The thermal sensors embedded with the drone can capture infrared images of crop fields, which can be used for crop stress, such as water and temperature (see Figure 1). ...
Food demands are increasing globally. Various issues such as urbanization, climate change, and desertification increasingly favour crop pests and diseases that limit crop productivity. Elaborating and discussing the pragmatic knowledge and information on recent advances in tools and techniques for crop monitoring developed in recent decades might help agronomists make more informed decisions. This chapter discusses the progress and development of new techniques equipped with recent sensors and platforms such as drones that have revolutionized the way of understanding plant physiology and stresses. It begins with the introduction to various tools available for crop stress estimation, mainly based on optical imaging such as multispectral, thermal, and hyperspectral imaging. An overview of unmanned aerial vehicle (UAV) -based image processing pipeline is presented and shed light on the possible avenues of UAV-based remote sensing for crop health monitoring using machine learning approaches.
... This is achieved through the utilization of sensors that are installed on diverse platforms such as satellites, airborne remote sensing devices (including manned drones and unmanned aerial vehicles), as well as ground-based equipment. Subsequently, these acquired data are processed by computers to support agricultural decision-making systems (Awais et al., 2022;Raihan & Tuspekova, 2022b;Huang et al., 2023). The examination of agriculture's geographical context can be approached by considering the varying levels of access that farmers have to livelihood capitals, local resources, and critical infrastructure and services within a certain geographic area (Wang et al., 2023;Raihan & Tuspekova, 2022c). ...
The aim of this study was to consolidate current information on the utilization of Geographic Information Systems (GIS) and Remote Sensing (RS) in the agricultural sector, with a focus on their role in promoting evidence-based policies and practices to enhance agricultural sustainability. Additionally, this review sought to identify the challenges hindering the widespread adoption of GIS and RS applications, particularly in low-and middle-income nations. This study employed the methodology of systematic literature review. The findings indicate that the utilization of GIS technology in the agricultural sector has experienced a notable increase over the past few years. The primary areas of use for GIS that have been identified encompass crop yield estimation, assessment of soil fertility, monitoring of cropping patterns, evaluation of drought conditions, detection and management of pests and crop diseases, implementation of precision agriculture techniques, and management of fertilizer and weed control. GIS technology possesses the capacity to augment the sustainability of agriculture by incorporating the spatial aspect of agricultural practices into agricultural policies. Furthermore, the potential of GIS in facilitating evidence-based decision making is expanding. Given the escalating peril of climate change on agriculture and food security, there exists a heightened imperative to include GIS into policy formulation and decision-making processes to enhance the sustainability of agricultural practices. The findings of this study might be beneficial in informing the development of policies that effectively integrate sustainable and climate-smart practices in agriculture.
... On top of the aforementioned studies, remote sensing can help obtain and analyze data without physical interaction with the object [21], as it is cheaper than most conventional methods. Measurements of surface reflectance or radiation can be detected by mounting a sensor on a satellite or a drone [22]. ...
Many insects attack date palm trees but date palm trees in the Sultanate are particularly under threat due to the spread of pests and the Dubas bug (Db). Date palm productivity in Oman has been reduced by 28% due to Db infestation. The manual field detection of these pests requires huge efforts and costs, making field surveys time consuming and difficult. In this context, remote sensing integrated with deep learning techniques can help in the early detection of Db infestation. A total of 240 date palms with corrected geospatial locations and coordinates and their health status were systematically recorded throughout the 66-square-kilometer study area. We used advanced remote sensing tools and deep learning techniques to detect individual palm trees and their health levels in terms of Db infestation. Very-high-resolution (50 cm) satellite images rendered in visible and NIR bands were used as datasets to delineate and identify individual tree positions and determine their health condition. Our proposed method resulted in an overall accuracy of 87% for the detection of date palm trees and 85% for the detection of health levels of the plants. The overall detection accuracy of high and low infestation levels was observed with high precision at 95% and 93%, respectively. Hence, we can conclude with confidence that our technique performed well by accurately detecting individual date palm trees and determining their level of Db infestation. The approach used in this study can also provide farmers with useful knowledge regarding the Db risk and damage control for better management of Db. Moreover, the model used in this study may also lay the foundations for other models to detect infested plants and trees other than date palms.
... One such technology that has made significant inroads into agriculture is the use of drones, or unmanned aerial vehicles (UAVs). Drones have the potential to revolutionize many aspects of farming by providing a cost-effective, scalable, and versatile platform for capturing highresolution, real-time data about crop conditions (Awais et al., 2022). Equipped with advanced sensors, these flying robots can map fields in fine detail, track crop health, assess soil conditions, and even administer targeted treatments. ...
It explores the enhancement of soil sampling techniques for smart agriculture, specifically focusing on efficient soil moisture acquisition through drone technology. The role of drone technology in advancing soil sampling practices, including its use for capturing soil moisture data. The comparative analysis between conventional and drone-based soil sampling showed the latter's superiority in efficiency, labor reduction, access to hard-to-reach areas, and sampling frequency. Case studies demonstrate successful implementation and confirm the increased efficiency and accuracy of drone-based techniques. Despite these hurdles, the future potential is promising, with AI, machine learning, sensor Modern Agriculture: Exploring Current Trends 2 | P a g e miniaturization, and technology integration expected to further augment drone-based soil sampling and moisture acquisition. By overcoming these challenges and harnessing the potential of drones, precision agriculture can be significantly improved, supporting sustainable farming practices. This chapter provides a comprehensive understanding of the current state and future potential of drone technology in soil sampling, paving the way for further research and implementation.
... If the NIR and RE bands are analysed the differences between classes are higher. Lower reflectance values are presented in Class 2, the literature confirms this effect (Awais et al., 2022). On the other hand, when the multispectral images (5 bands) were used as input in the CNN the accuracy in the validation process increased to a value of 71%. ...
... The main areas using modern technical means in agriculture are agriculture for ecosystem services, phenotyping, agricultural land use monitoring, crop yield forecasting, and crop monitoring for yield optimization (precision farming) [8,14,15]. The main tasks for modern technical means in precision farming include weed [16][17][18] and disease [8,19] detection and diagnosis, nutrient [20,21] and water stress [22,23] detection, and soil property diagnosis for their optimization [24,25]. Among these tasks, the accurate and early estimation of plant pest and disease spreading and harmfulness is one of the most important for intensive crop production, the breeding of new varieties, and pesticide usage regulation [8]. ...
The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.
Forest canopies are critical components of terrestrial ecosystems, influencing various ecological functions such as carbon cycling, climate regulation, and biodiversity preservation. Accurate monitoring of forest canopy characteristics, such as leaf area index (LAI), vegetation health, and canopy density, is essential for assessing forest health and supporting sustainable management strategies. However, traditional methods such as ground-based surveys and satellite remote sensing have limitations, including high costs, accessibility constraints, and insufficient spatial or temporal resolution. These challenges highlight the need for more effective and efficient methods of canopy monitoring. Drone-based spectral data analysis offers a promising solution to these challenges, providing high-resolution, cost-effective, and efficient methods for evaluating forest canopy characteristics. Equipped with multispectral and hyperspectral sensors, drones are capable of capturing fine-grained data on canopy health, structure, and productivity. This study investigates the application of drone technology in assessing forest canopies, focusing on key metrics such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and leaf area index (LAI). The methodology involved conducting drone flights across diverse forest ecosystems to collect spectral data, which was then processed to extract relevant canopy metrics. The results demonstrate that drone-based spectral analysis significantly enhances the accuracy and efficiency of forest canopy evaluation, particularly in detecting subtle variations in vegetation health and structural attributes. In comparison to traditional methods, drone-based analysis provides higher spatial resolution, faster data collection, and greater accessibility to remote areas. Despite the numerous advantages, the study also highlights certain challenges, such as the complexity of data processing, the need for technical expertise, and the high initial investment in drone technology. Nevertheless, the findings suggest that drone-based spectral data analysis has the potential to transform forest monitoring practices, offering a scalable and reliable tool for forest management, conservation, and climate change adaptation. The increasing global focus on climate change and ecosystem preservation has highlighted the need for accurate and efficient monitoring techniques for forest ecosystems. As forest management becomes increasingly complex, the ability to quickly and accurately assess canopy characteristics is crucial for maintaining ecosystem health and biodiversity. Traditional monitoring methods often fall short in terms of providing timely, high-resolution data across large or remote areas. Drone-based spectral analysis, on the other hand, offers an accessible and scalable alternative to these conventional approaches. By using drones equipped with advanced spectral sensors, researchers can collect high-resolution imagery and data on a variety of forest canopy parameters, from leaf health to canopy structure. Furthermore, drones allow for frequent, real-time monitoring, enabling dynamic, up-to-date insights into forest conditions, which can be vital for effective forest management and conservation planning.
Agriculture is increasingly challenged by environmental stressors, including drought, pest outbreaks, and climate variability, which significantly impact crop yields and food security. To address these challenges, satellite imagery combined with spectral indices has emerged as a revolutionary tool for monitoring and detecting stress in agricultural systems. Spectral indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), provide critical insights into vegetation health by analyzing satellite-captured reflectance data. This article explores the integration of satellite imagery and spectral indices to detect environmental stress in agriculture, detailing methodologies for data processing, real-world case studies, and the potential of emerging technologies. By leveraging these tools, farmers, policymakers, and researchers can enhance decision-making, mitigate losses, and promote sustainable agricultural practices. In recent years, the intersection of advanced satellite technologies and agricultural management has gained prominence due to the growing demand for precision farming solutions. By detecting stress factors early, satellite imagery allows stakeholders to implement timely interventions, reducing the risk of large-scale crop failures. Moreover, the integration of spectral indices with Geographic Information Systems (GIS) enables detailed spatial analysis, which is critical for understanding stress patterns across diverse landscapes. This approach not only enhances productivity but also supports sustainable practices by minimizing the overuse of resources like water and fertilizers. The study underscores the transformative potential of these tools in modern agriculture. The ability to remotely monitor vast agricultural landscapes has opened new avenues for managing resources more efficiently. Spectral indices, derived from satellite imagery, offer a non-invasive and scalable solution to assess vegetation health, water stress, and soil conditions. By applying these indices, researchers can quantify the severity and extent of environmental stress with remarkable precision. Furthermore, combining satellite data with machine learning algorithms has enabled predictive modeling, empowering farmers to anticipate stress events before they escalate. This proactive approach not only reduces economic losses but also contributes to climate-resilient agricultural systems, addressing the pressing need for global food security in an era of uncertainty.
Optimizing agricultural water resource management is crucial for food production, as effective water management can significantly improve irrigation efficiency and crop yields. Currently, precise agricultural water demand forecasting and management have become key research focuses; however, existing methods often fail to capture complex spatial and temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model to effectively integrate spatial and temporal features from MODIS and GLDAS datasets. Our model leverages the high-resolution spatial data from UNet and the temporal dependencies captured by ConvLSTM to significantly improve prediction accuracy. Experimental results demonstrate that our UCL model achieves the best R2 compared to existing methods, reaching 0.927 on the MODIS dataset and 0.935 on the GLDAS dataset. This approach highlights the potential of AI and remote sensing technologies in addressing critical challenges in agricultural water management, contributing to more sustainable and efficient food production systems.
The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming explores how cutting-edge technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain are transforming farming for a sustainable future. Addressing challenges such as climate change, resource scarcity, and food supply chain inefficiencies, the book highlights how these technologies can improve decision-making, enhance crop yields, and increase transparency in agriculture. With a blend of theory and real-world applications, it covers everything from AI-driven pesticide prediction and disease identification to using Blockchain for efficient food supply chain management. This comprehensive guide is essential for researchers, professionals, and anyone interested in the intersection of technology and sustainable farming. Key Features: - Introduction to Digital Twin technology for sustainable farming - Practical applications of AI and IoT in agriculture - Blockchain's role in food supply chain management - Frameworks for precision agriculture and access to government schemes - Insights on integrating AI, IoT, and Blockchain into solid waste management systems
Timely and accurate acquisition of field crop growth and environmental information is essential for effective and accurate crop management. Low-altitude unmanned aerial system (UAS) remote sensing technology offers unparalleled advantages in obtaining crop images of different scales making it a vital tool for agricultural information monitoring. Additionally, machine learning (ML) and deep learning (DL) have significantly enhanced the efficiency and potential of UAS, especially for agricultural monitoring. This review aims to provide a comprehensive analysis by integrating bibliometric insights and thematic exploration to deepen our understanding of the research landscape and guide future investigations. We check commonly used UAS types and sensors, summarizing the advantages of UAS remote sensing in agricultural monitoring. Furthermore, critical examinations of the shortcomings observed in current research were also discussed. To further advance and optimize technology, analysis and monitoring methods, we identify crucial future research needs and directions: (1) Advancements in equipment and application scenarios, (2) Enhanced exploration of UAS multi-source data fusion, (3) Developments in methodology for agricultural monitoring, and (4) Expanding the scope of remote sensing applications in agricultural monitoring. The first two advancements will provide sufficient discriminative information, while the latter improvements will enhance the efficiency and generalizability of ML and DL methods for processing multi-platform remote sensing data. These developments will broaden the application of low-altitude UAS remote sensing in precision agriculture for improved crop information acquisition and analysis.
This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the “black-box” nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.
Robotic fruit harvesting holds potential in precision agriculture to improve harvesting efficiency. While ground mobile robots are mostly employed in fruit harvesting, certain crops, like avocado trees, cannot be harvested efficiently from the ground alone. This is because of unstructured ground and planting arrangement and high-to-reach fruits. In such cases, aerial robots integrated with manipulation capabilities can pave new ways in robotic harvesting. This paper outlines the design and implementation of a bimanual UAV that employs visual perception and learning to autonomously detect avocados, reach, and harvest them. The dual-arm system comprises a gripper and a fixer arm, to address a key challenge when harvesting avocados: once grasped, a rotational motion is the most efficient way to detach the avocado from the peduncle; however, the peduncle may store elastic energy preventing the avocado from being harvested. The fixer arm aims to stabilize the peduncle, allowing the gripper arm to harvest. The integrated visual perception process enables the detection of avocados and the determination of their pose; the latter is then used to determine target points for a bimanual manipulation planner. Several experiments are conducted to assess the efficacy of each component, and integrated experiments assess the effectiveness of the system.
Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.
A promising approach to lowering winter heating requirements is the use of ground-coupled heat exchange systems. This is especially important given the rising cost of energy and the pressing need to address global warming and environmental harm brought on by the use of fossil fuels. In this study, we investigated the capability of ground-coupled systems in the Pakistan Cholistan desert. With high-density polyethylene (HDPE) pipework, we focused on important parameters such air intake and output temperatures, humidity, and airflow velocity. According to our research, the system generates a notable 18 °C temperature differential between the input and output pipes while it is operating at night. Despite fluctuations in external temperature, this temperature difference during daytime operation remains constant at 17 °C. The system maintains an average temperature differential of 14 °C throughout the day and night, demonstrating its ability to deliver reliable heating. Additionally, a drop in airspeed from 8 to 5 m/s causes a temperature increase of roughly 0.5 °C. This condition is linked to increased air interaction within the pipeline at slower speeds. During the winter season, the inlet air exhibits a humidity level ranging from 80 to 90%, which subsequently decreases to 48 to 58% after passing through the pipe system. This variation in humidity levels between the inlet and outlet is due to the temperature difference between these points, resulting in a comfortable indoor environment suitable for various activities. Our research carries significant implications for reducing reliance on depleting and costly fossil fuels in the region, while also providing a sustainable green energy solution. Future research avenues include exploring novel pipe materials and varying pipe system depths to enhance heat transfer efficiency. Additionally, the integration of hybrid green energy solutions, such as coupling air turbines with solar or wind energy, holds promise for further investigation.
Geospatial technologies have emerged as a crucial component in the agriculture sector of India, driven by the advancements in electronic technology and the decreasing costs of computer and mobile devices. Indian agriculture confronts challenges such as severe climate events, pest invasions, nutrient management, and carbon emissions. To address these challenges, the adoption of new farming techniques is essential. The integration of new digital platforms into traditional agricultural practices has proven to be highly beneficial. These platforms assist farmers in various aspects of their agricultural practices, leading to reduced crop distress, enhanced yield, and improved overall efficiency. With the aid of digital agriculture, real-time information about farmland requirements can be gathered and analyzed. Geospatial technologies play a critical role in handling this data, providing valuable insights and decision support at various scales. By leveraging these technologies, farmers can make informed decisions and take appropriate actions to optimize productivity and market value. The Digital Agriculture Mission 2021–2025 has been designed with a specific focus on delivering agri-focused solutions that directly benefit farmers. The mission aims to increase farmers’ income, enabling them to contribute significantly to the nation's economic growth and food security. The mission recognizes the potential of digital technologies in transforming the agriculture sector and seeks to harness their power through strategic initiatives and partnerships. Over the past two decades, the development and research in precision agriculture have made significant contributions to the Indian agriculture sector. Precision agriculture leverages spatial and temporal data related to crops and the environment, enabling farmers to achieve greater productivity and market value. By utilizing advanced technologies and analytical tools, precision agriculture facilitates efficient resource management, optimal decision-making, and targeted interventions. This chapter provides an in-depth technological review of the utilization of geospatial technologies in the agriculture sector of India. By understanding the potential and benefits of geospatial technologies, stakeholders can effectively leverage them to drive the transformation and growth of the agriculture sector in India.
Predicting the future of drones and their operational characteristics, including onboard computing, propulsion systems, and battery longevity, is fraught. We know that all of these system elements will improve, but the specifics of these improvements are somewhat elusive. New advancements in these domains happen frequently and they are routinely novel and unexpected. However, efforts considering how society might apply UAV technology to conquer existing local, regional, and global challenges are less elusive and easier to prognosticate. This chapter explores UAVs and their future applications within the domains of geospatial intelligence, disaster response, precision agriculture, medical services, and commerce and logistics. In addition, this chapter provides concluding thoughts and discusses policy challenges and implications within the context of individual privacy.
When the model is applied to a new area, the first thing to consider is the problem of localization correction, and parameter sensitivity analysis is an important part of it. In this study, the AquaCrop model was used to simulate the growth process of tea trees, and the parameter sensitivity of the model was tested by the method of EFAST. The results show that the crop parameters with high sensitivity to AGB are mainly KCB, wp, Tbase, den, and Hi; the crop parameters with high sensitivity to CC are mainly CGC, den, Tbase, mcc, wp, and KCB; the crop parameters with high sensitivity to Y are mainly The main crop parameters are Hi, den, wp, KCB, CGC, and Tbase. Overall, the AquaCrop model is mainly affected by the crop canopy coefficient, phenological development coefficient, biomass coefficient, and yield coefficient. Considering that sensitive parameters greatly influence the output, the sensitive parameters should be calibrated first, and the insensitive parameters can be set to fixed values to reduce the workload. More attention should be paid to the interaction between parameters when studying crop models. This helps to correctly understand the structure of the crop model and the role of each parameter, achieve an accurate simulation of the growth process, and improve the simulation accuracy.
Nowadays, research works into the dynamic and static human activities on Smart spaces abounds. Artificial Intelligence (AI) and low cost non-privacy invasive ambient sensors have made this ubiquitous. This review presents a state-of-the-art analysis, performance evaluation, and future research direction. One of the aims of activity recognition (especially that of humans) systems using thermal sensors and AI is the safety of persons in Smart spaces. In a Smart home, human activity detection systems are put in place to ensure the safety of persons in such an environment. This system should have the ability to monitor issues like fall detection, a common home-related accident. In this work, a review of trends in thermal sensor deployment, an appraisal of the popular datasets, AI algorithms, testbeds, and critical challenges of the recent works was provided to direct the research focus. In addition, a summary of AI models and their performance under various sensor resolutions was presented.
Remote sensing (RS) techniques offer advantages over other methods for measuring soil properties, including large-scale coverage, a non-destructive nature, temporal monitoring, multispectral capabilities, and rapid data acquisition. This review highlights the different detection methods,types, parts, and applications of RS techniques in soil measurements, as well as the advantages and disadvantages of the measurements of soil properties. The choice of the methods depends on the
specific requirements of the soil measurements task because it is important to consider the advantages
and limitations of each method, as well as the specific context and objective of the soil measurements,
to determine the most suitable RS technique. This paper follows a well-structured arrangement after investigating the existing literature to ensure a well-organized, coherent review and covers all the essential aspects related to studying the advancement of using RS in the measurements of soil properties. While several remote sensing methods are available, this review suggests spectral reflectance,
which entails satellite remote sensing and other tools based on its global coverage, high spatial resolution, long-term monitoring capabilities, non-invasiveness, and cost effectiveness. Conclusively, RS has improved soil property measurements using various methods, but more research is needed for calibration, sensor fusion, artificial intelligence, validation, and machine learning applications to enhance accuracy and applicability
Open biomass burning (OBB) in agriculture presents a significant and well-documented challenge, posing severe consequences for both environmental and human health. OBB releases air pollutants that degrade air quality and contribute to climate change, leading to premature deaths in regions with high concentrations of open crop straw burning (OCSB) emissions. Although policies aimed at prohibiting OBB are in place, the efficacy of these regulations in mitigating OCSB emissions remains ambiguous. Consequently, early prevention and monitoring of open biomass combustion are imperative for environmental preservation. Traditional monitoring techniques, reliant on fixed-position cameras, are constrained by their location and monitoring intensity, making concealed fire recognition a complex problem. To address this limitation and monitor the human living environment more flexibly and accurately, we propose a new method to identify straw fires in UAV Aerial Image Using CNN Branch Reinforce Transformer which named BranTNet, enabling early detection and rapid response to crop straw fires. By integrating computer vision technology and deep learning algorithms, straw fires in UAV-acquired aerial survey images can be detected and categorized. In the realm of artificial intelligence algorithms, we skillfully merge convolution and attention mechanisms, harnessing the full potential of both methodologies. Moreover, we seamlessly incorporate transfer learning, skillfully unifying self-training convolution modules with pre-trained transformer modules. This strategic amalgamation not only minimizes time costs but also ensures optimal experimental outcomes. Regarding data, we meticulously collected a substantial number of authentic samples, ensuring the sufficiency of our experimental dataset. The experimental results demonstrate that our proposed method exhibits exceptional accuracy and robustness in detecting and identifying straw fires in UAV aerial survey images. Our approach outperforms the use of convolution or attention mechanisms alone. By integrating this approach with drone technology, we unlock the potential for developing more versatile and precise monitoring solutions, expanding the application of drones to diverse domains. This progress contributes significantly to the early detection and prevention of crop straw fires, fundamentally reducing environmental pollution, curbing carbon emissions, and advancing the cause of carbon neutrality. This innovative technique for monitoring and preventing OBB holds substantial promise in mitigating the adverse effects of OBB on the environment and human health.
The yield gaps between conventional and progressive farms have widened considerably, globally. Precision farming, the use of site-specific agricultural inputs accurately through decision support mechanisms, has the ability to lower the potential crop yield gap. This chapter offers an outline on the development of precision agriculture technologies (PATs) and its current adoptability status based on published literature from the past two decades. The focus of this chapter is mainly on the transformation of agriculture from mechanized to precision agriculture (PA). The role of PA, variability management zones, resources variability, sensors innovation in agricultural engineering, and the development of precision agricultural machinery to maximize the farm output will be discussed. Moreover, recent development of remote sensing and GIS applications in agriculture, use of unmanned aerial vehicles (UAVs) for agriculture, global application of PA, adoption tendency of PAT, and potential for adopting the precision agricultural technologies in developing countries (i.e., Pakistani farm settings) to modernize the conventional farming and maximize the farm yield are also discussed. There have been a number of advanced countries that already have adopted the PAT, including the United States and Europe, in the 1980s and 1990s. However, in developing countries, farmers are still conservative to use these technologies either due to high technology cost or otherwise, less adoptability to new technologies. In this chapter, we have examined the problems and recommend possible solutions to the developing countries for worldwide adoptability of PATs. The researchers and farmers can follow the recommended suggestions for the adoptability of PATs to maximize the farm income, which will ensure the global food security.
The motivation for this review paper came from the developing countries where the economy is mostly dependent on agriculture and climate conditions. Based on current conditions and historical records, profitability in production farming depends on making a right and timely operational decision. Precision farming is a systematic program designed to maximize the productivity of agriculture by carefully tailoring the soil and crop management to meet the specific requirements in each field while preserving environmental quality. This review paper highlights the development of an automated irrigation system with portable wireless sensor networks and decision support methods to remotely measure the environmental parameters in an agriculture field. Radio satellite, mobile phones, sensors, internet-based communication, and microcontroller capture the ecological parameters such as soil moisture, temperature, humidity, and light intensity. The knowledge gained from the sensors is transferred directly to the cloud server by using IoT technology. Users from anywhere in the world can display them through an internet-enabled device. Development of sensor-based application in modern agriculture makes it cost-effective and potentially productive and increases the efficiency through precision agriculture farming. Different limitations have been reported in the previously reviewed publications like the shortage of power in the field that can be solved by using a solar panel that recharges the battery at the same time using electricity. Bluetooth application in the agriculture sector is mainly improved by design system optimization. Problems related to transmission and radio range frequency can be solved by using a power class upgraded antenna.
Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.
Excessive agro-chemicals are being used to enhance agricultural production to meet the food security challenge with increasing population. Gaps or overlaps in the spraying patterns can result in under or over application of pesticides thus causing environmental hazards. Conventional sprayers have uniform spray drift that is less effective for heavy canopies. Alternatively, unmanned aerial system (UAS) are being used for crop monitoring and management (spraying insecticides/pesticides) however proper spray height and nozzle opening needs investigation. In this study, a hexa copter unmanned aerial agro-chemical spraying (UAAS) system was tested at different heights and nozzle openings to determine spray uniformity in the field conditions. Operating height of 1.5 m, 2.0 m, 2.5 m and 3.0 m with spray nozzle opening (discharge rate) percentages of 25, 50, 75 and 100% were investigated for variable wind speeds ranging between 1 m/s to 5.8 m/s. Colored spray was used to get impression of spraying droplets on sensitive papers placed for the purpose. Spray dots were counted to get the spraying uniformity at different heights and wind conditions. The hexa copter UAAS system provided a good uniformity and coverage area at operating height of 1.5 m with 50, 75 and 100% spray nozzle opening under wind conditions varying from 1 m/sec to 5.8 m/sec. This investigation provides a guideline to the farmers and service providers entering into the business of spraying through UAS.
Remote sensing has several advantages in the field of agronomical research purpose. The assessment of agricultural crop canopies has provided valuable insights in the agronomic parameters. Remote sensing play a significant role in crop classification, crop monitoring and yield assessment. The use of remote sensing is necessary in the field of agronomical research purpose because they are highly vulnerable to variation in soil, climate and other physico- chemical changes. The monitoring of agricultural production system follows strong seasonal patterns in relation to the biological life cycle of crops. All these factors are highly variable in space and time dimensions. Moreover, the agricultural productivity can change within short time periods, due to unfavourable growing conditions. Monitoring of agricultural systems should be followed in timely. Remote sensing are important tools in timely monitoring and giving an accurate picture of the agricultural sector with high revisit frequency and high accuracy. For sustainable agricultural management, all the factors which are influencing the agricultural sector need to be analysed on spatio-temporal basis. The remote sensing along with the other advanced techniques such as global positioning systems and geographical information systems are playing a major role in the assessment and management of the agricultural activities. These technologies have many fold applications in the field of agriculture such as crop acreage estimation, crop growth monitoring, soil moisture estimation, soil fertility evaluation, crop stress detection, detection of diseases and pest infestation, drought and flood condition monitoring, yield estimation, weather forecasting, precision agriculture for maintaining the sustainability of the agricultural systems and improving the economic growth of the country
We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.
Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.
Irrigation water management and real-time monitoring of crop water stress status can enhance agricultural water use efficiency, crop yield, and crop quality. The aim of this study was to simplify the calculation of the crop water stress index (CWSI) and improve its diagnostic accuracy. Simplified CWSI (CWSIsi) was used to diagnose water stress for cotton that has received four different irrigation treatments (no stress, mild stress, moderate stress, and severe stress) at the flowering and boll stage. High resolution thermal infrared and multispectral images were taken using an Unmanned Aerial Vehicle remote sensing platform at midday (local time 13:00), and stomatal conductance (gs), transpiration rate (tr), and cotton root zone soil volumetric water content (θ) were concurrently measured. The soil background pixels of thermal images were eliminated using the Canny edge detection to obtain a unimodal histogram of pure canopy temperatures. Then the wet reference temperature (Twet), dry reference temperature (Tdry), and mean canopy temperature (Tl) were obtained from the canopy temperature histogram to calculate CWSIsi. The other two methods of CWSI evaluation were empirical CWSI (CWSIe), in which the temperature parameters were determined by measuring natural reference cotton leaves, and statistical CWSI (CWSIs), in which Twet was the mean of the lowest 5% of canopy temperatures and Tdry was the air temperature (Tair) + 5 °C. Compared with CWSIe, CWSIs and spectral indices (NDVI, TCARI, OSAVI, TCARI/OSAVI), CWSIsi has higher correlation with gs (R2 = 0.660) and tr (R2 = 0.592). The correlation coefficient (R) for θ (0–45 cm) and CWSIsi is also high (0.812). The plotted high-resolution map of CWSIsi shows the different distribution of cotton water stress in different irrigation treatments. These findings demonstrate that CWSIsi, which only requires parameters from a canopy temperature histogram, may potentially be applied to precision irrigation management.
Nondestructive monitoring of the biophysical properties of crops can quickly and accurately understand its response to various environmental impacts. The chlorophyll content of crops is an important factor of crop health, so crop yields can be estimated. In present study, the kinnow orchard chlorophyll content estimation had been evaluated by using high resolution multispectral UAVs coupled with ground-based information for accurately monitoring the kinnow chlorophyll status real time at Kotmomin, district Sargodha. The UAVs Images were acquired during the same time when ground-truthing campaign for chlorophyll contents was performed. Vegetation indices including Normalized Difference Vegetation Index (NDVI), Transformed Normalized Difference Vegetation Index (TNDVI), Modified Chlorophyll Absorbed Ratio Index (MCARI2), Soil adjusted vegetation Index (SAVI) and Modified soil adjusted vegetation index (MSAVI2) were derived by multispectral UAVs images for chlorophyll estimation. Validation of the chlorophyll contents were performed at 46 the kinnow trees leaves non-destructively using chlorophyll meter (SPAD-502 Minolta) along with GPS positions during April 26, 2018 at Kotmomin. The regression analysis were performed between ground-truthing data and UAVs derived vegetation indices for predicting accuracy of the model. MSAVI2 and TNDVI were proved to be sensitive indices to estimate the chlorophyll content in the kinnow orchard with highest coefficient of determination (R 2) 0.89 and 0.85 respectively. The results showed that the use of high spatial resolution UAVs to obtain chlorophyll content can accurately and quickly assess crop health status in a wider range which will help in managing crop nutrition requirement in real time in the kinnow orchard.
Precision horticulture: Peachy method for monitoring orchard growth A rapid automated system for monitoring peach tree growth had been developed that could replace laborious field measurements and enable farmers to manage their orchards more effectively. Knowing a tree’s crown width—the mass of branches and foliage growing outwards from its trunk—enables more effective spraying, machine harvesting, and assessment of trees’ growth. However, because peach trees have irregular shaped crowns, there is no rapid and reliable method for extracting this information. Wei Guo at the University of Tokyo and colleagues used unmanned aerial vehicles to photograph an orchard, then generated a digital surface model which enabled individual trees to be identified, and their crown widths and projection areas to be accurately calculated. This revealed that the trees grew faster during early summer compared to late summer, with wide variation between individual trees.
In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the ‘journey’ of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.
Agriculture is critical to sustainable development, and agricultural production by smallholders in lower-income countries contributes substantially to the food security of both rural and urban populations. Smallholders face many challenges related to urbanisation, sustainable use of natural resources and climate change. While there will be different ways of achieving sustainable agricultural systems, all will involve adaptation on a massive scale to meet the future food production challenge at greatly reduced carbon cost. Here we evaluate recent progress in agricultural adaptation using surveys in five regions, 21 countries and 45 sites, covering 315 villages and approximately 6300 households. These surveys include information about the changes made to farming systems in the last decade. We synthesise this information by grouping households into types: food insecure, hanging in, stepping up and stepping out. We then profile each site in terms of its proportion of households of these four types. While farmers have made some changes in their farming practices over the last decade, most changes have been incremental and piecemeal. We find little evidence in the study sites in any region of farming changes at the scale needed to enhance food security of significant proportions of the population. Searching for commonalities in the enabling environment among sites with similar proportions of household types, we found collective action at the community level coupled with appropriate climate information provision and the active participation of local organisations to be associated with higher levels of food security. To foster the changes needed in agricultural and food systems, a much stronger focus will be required on the enabling environment if food security goals are to be attainable. Without more nuanced and effective targeting of technological as well as institutional interventions, agricultural adaptation to meet the food production challenge in the coming decades is unlikely to occur.
Unmanned aerial vehicles (UAV) provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect on fruiting, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. To establish the response of pruning in an orchard of lychee trees, an assessment of changes in tree structure, i.e., tree crown perimeter, width, height, area and Plant Projective Cover (PPC), was undertaken using multi-spectral UAV imagery collected before and after a pruning event. While tree crown perimeter, width and area could be derived directly from the delineated tree crowns, height was estimated from a produced canopy height model and PPC was most accurately predicted based on the NIR band. Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in tree crown perimeter of 1.94 m, tree crown width of 0.57 m, tree crown height of 0.62 m, tree crown area of 3.5 m², and PPC of 14.8%. In order to provide guidance on data collection protocols for orchard management, the impact of flying height variations was also examined, offering some insight into the influence of scale and the scalability of this UAV-based approach for larger orchards. The different flying heights (i.e., 30, 50 and 70 m) produced similar measurements of tree crown width and PPC, while tree crown perimeter, area and height measurements decreased with increasing flying height. Overall, these results illustrate that routine collection of multi-spectral UAV imagery can provide a means of assessing pruning effects on changes in tree structure in commercial orchards, and highlight the importance of collecting imagery with consistent flight configurations, as varying flying heights may cause changes to tree structural measurements.
Precision viticulture has arisen in recent years as a new approach in grape production. It is based on assessing field spatial variability and implementing site-specific management strategies, which can require georeferenced information of the three dimensional (3D) grapevine canopy structure as one of the input data. The 3D structure of vineyard fields can be generated applying photogrammetric techniques to aerial images collected with Unmanned Aerial Vehicles (UAVs), although processing the large amount of crop data embedded in 3D models is currently a bottleneck of this technology. To solve this limitation, a novel and robust object-based image analysis (OBIA) procedure based on Digital Surface Model (DSM) was developed for 3D grapevine characterization. The significance of this work relies on the developed OBIA algorithm which is fully automatic and self-adaptive to different crop-field conditions, classifying grapevines, and row gap (missing vine plants), and computing vine dimensions without any user intervention. The results obtained in three testing fields on two different dates showed high accuracy in the classification of grapevine area and row gaps, as well as minor errors in the estimates of grapevine height. In addition, this algorithm computed the position, projected area, and volume of every grapevine in the field, which increases the potential of this UAV- and OBIA-based technology as a tool for site-specific crop management applications.
In light of climate change and its impacts on plant physiology, optimizing water usage and improving irrigation practices play a crucial role in crop management. In recent years, new optical remote sensing techniques have become widespread since they allow a non-invasive evaluation of plant water stress dynamics in a timely manner. Unmanned aerial vehicles (UAV) currently represent one of the most advanced platforms for remote sensing applications. In this study, remote and proximal sensing measurements were compared with plant physiological variables, with the aim of testing innovative services and support systems to farmers for optimizing irrigation practices and scheduling. The experiment, conducted in two vineyards located in Sardinia, Italy, consisted of two regulated deficit irrigation (RDI) treatments and two reference treatments maintained under stress and well-watered conditions. Indicators of crop water status (Crop Water Stress Index-CWSI-and linear thermal index) were calculated from UAV images and ground infrared thermal images and then related to physiological measurements. The CWSI values for moderate water deficit (RDI-1) were 0.72, 0.28 and 0.43 for 'Vermentino', 'Cabernet' and 'Cagnulari' respectively, while for severe (RDI-2) water deficit the values were 0.90, 0.34 and 0.51. The highest differences for net photosynthetic rate (Pn) and stomatal conductance (Gs) between RDI-1 and RDI-2 were observed in 'Vermentino'. The highest significant correlations were found between CWSI with Pn (R = -0.80), with ΦPSII (R = -0.49) and with Fv'/Fm' (R = -0.48) on 'Cagnulari', while a unique significant correlation between CWSI and non-photochemical quenching (NPQ) (R = 0.47) was found on 'Vermentino'. Pn, as well as the efficiency of light use by the photosystem II (PSII), declined under stress conditions and when CWSI values increased. Under the experimental water stress conditions, grapevines were able to recover their efficiency during the night, activating a photosynthetic protection mechanism such as thermal energy dissipation (NPQ) to prevent irreversible damage to the photosystem. The results presented here demonstrate that CWSI values derived from remote and proximal sensors could be valuable indicators for the assessment of the spatial variability of crop water status in Mediterranean vineyards.
This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas.
Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.
Water stress which affects yield and wine quality is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on an unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500-800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56-0.87 with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model was able to estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of -9.1%. For the validation of CMI, MAE, RMSE and RE values were between 0.26-0.27 MPa, 0.32-0.34 MPa and -24.2-25.6%, respectively
Precision irrigation management is based on the accuracy and feasibility of sensor data assessing the plant water status. Multispectral and thermal infrared images acquired from an unmanned aerial vehicle (UAV) were analyzed to evaluate the applicability of the data in the assessment of variants of subsurface irrigation configurations. The study was carried out in a Cabernet Sauvignon orchard located near Benton City, Washington. Plants were subsurface irrigated at a 30, 60, and 90 cm depth, with 15%, 30%, and 60% irrigation of the standard irrigation level as determined by the grower in commercial production management. Half of the plots were irrigated using pulse irrigation and the other half using continuous irrigation techniques. The treatments were compared to the control plots that received standard surface irrigation at a continuous rate. The results showed differences in fruit yield when the control was compared to deficit irrigated treatments (15%, 30%, 60% of standard irrigation), while no differences were found for comparisons of the techniques (pulse, continuous) or depths of irrigation (30, 60, 90 cm). Leaf stomatal conductance of control and 60% irrigation treatments were statistically different compared to treatments receiving 30% and 15% irrigation. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and canopy temperature were correlated to fruit yield and leaf stomatal conductance. Significant correlations (p < 0.01) were observed between NDVI, GNDVI, and canopy temperature with fruit yield (Pearson's correlation coefficient, r = 0.68, 0.73, and -0.83, respectively), and with leaf stomatal conductance (r = 0.56, 0.65, and -0.63, respectively) at 44 days before harvest. This study demonstrates the potential of using low-altitude multispectral and thermal imagery data in the assessment of irrigation techniques and relative degree of plant water stress. In addition, results provide a feasibility analysis of our hypothesis that thermal infrared images can be used as a rapid tool to estimate leaf stomatal conductance, indicative of the spatial variation in the vineyard. This is critically important, as such data will provide a near real-time crop stress assessment for better irrigation management/scheduling in wine grape production.
The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability, including sectors under full irrigation and deficit irrigation over nectarine and peach orchards at 6.12 cm ground sample distance. The study site was classified into sub-regions based on crop properties, such as cultivars and tree training systems. In order to enhance the accuracy of the mapping, edge extraction and filtering were conducted prior to the probability modelling employed to obtain crop-property-specific (‘adaptive’ hereafter) lower and higher temperature references (Twet and Tdry respectively). Direct measurements of stem water potential (SWP, ψstem) and stomatal conductance (gs) were collected concurrently with UAV remote sensing and used to validate the thermal index as crop biophysical parameters. The adaptive crop water stress index (CWSI) presented a better agreement with both ψstem and gs with determination coefficients (R2) of 0.72 and 0.82, respectively, while the conventional CWSI applied by a single set of hot and cold references resulted in biased estimates with R2 of 0.27 and 0.34, respectively. Using a small number of ground-based measurements of SWP, CWSI was converted to a high-resolution SWP map to visualize spatial distribution of the water status at field scale. The results have important implications for the optimal management of irrigation for crops.
This paper reviews the literature on applications of remote sensing for monitoring soil- and
crop- water status for irrigation purposes. The review is organized into two main sections: (1) sensors
and platforms applied to irrigation studies and (2) remote sensing approaches for precision irrigation
to estimate crop water status, evapotranspiration, infrared thermography, soil and crop characteristics
methods. Recent literature reports several remote sensing (RS) approaches to monitor crop water
status in the cultivated environment. Establishing the right amount of water to supply for different
irrigation strategies (maximization of yield or water use efficiency (WUE)) for a large number of
crops is a problem that remains unresolved. For each crop, it will be necessary to create a stronger
connection between crop-water status and crop yield.
Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry.
We conducted a study in a large almond farm in Merced County, California, to monitor water status by using high-resolution multi-spectral imagery accquired by a Small Unmanned Aerial System (sUAS). More specifically, we would like to predict Stem Water Potential (SWP) via canopy Normalized Difference Vegetation Index (NDVI). During 2014, an aircraft equipped with multi-spectral cameras flew over the orchard weekly throughout the growing season. At the same time, SWP was measured for the sample trees under five different water treatment levels. Instead of averaging pixels in an orchard level, a block level or a canopy level, pixels were analyzed in the sub-canopy level to obtain canopy NDVI. An improved correlation between SWP and canopy NDVI was obtained by applying lower NDVI threshold. The relationship between SWP and canopy NDVI was also discussed at different growing stages–fruit development and post-harvest. However, tests of equality of distribution indicated that canopy NDVI distributions from different flights within a day were significantly different. Therefore, further calibration regarding the effects of solar motion on canopy NDVI is necessary.
Smart Farming is a development that emphasizes the use of information and communication technology in the cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing are expected to leverage this development and introduce more robots and artificial intelligence in farming. This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following a structured approach, a conceptual framework for analysis was developed that can also be used for future studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in roles and power relations among different players in current food supply chain networks. The landscape of stakeholders exhibits an interesting game between powerful tech companies, venture capitalists and often small start-ups and new entrants. At the same time there are several public institutions that publish open data, under the condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated food supply chain or 2) open, collaborative systems in which the farmer and every other stakeholder in the chain network is flexible in choosing business partners as well for the technology as for the food production side. The further development of data and application infrastructures (platforms and standards) and their institutional embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective, the authors propose to give research priority to organizational issues concerning governance issues and suitable business models for data sharing in different supply chain scenarios.
In the context of precision viticulture, remote sensing in the optical domain offers a potential way to map crop structure characteristics, such as vegetation cover fraction, row orientation or leaf area index, that are later used in decision support tools. A method based on the RGB color model imagery acquired with an unmanned aerial vehicle (UAV) is proposed to describe the vineyard 3D macro-structure. The dense point cloud is first extracted from the overlapping RGB images acquired over the vineyard using the Structure from Motion algorithm implemented in the Agisoft PhotoScan software. Then, the terrain altitude extracted from the dense point cloud is used to get the 2D distribution of height of the vineyard. By applying a threshold on the height, the rows are separated from the row spacing. Row height, width and spacing are then estimated as well as the vineyard cover fraction and the percentage of missing segments along the rows. Results are compared with ground measurements with root mean square error (RMSE) = 9.8 cm for row height, RMSE = 8.7 cm for row width and RMSE = 7 cm for row spacing. The row width, cover fraction, as well as the percentage of missing row segments, appear to be sensitive to the quality of the dense point cloud. Optimal flight configuration and camera setting are therefore mandatory to access these characteristics with a good accuracy.
The surface energy balance algorithm for land (SEBAL) estimates land surface evapotranspiration (ET) from radiometric surface temperature (TR), but requires manual selection of calibration pixels, which can be impractical for mapping seasonal ET. Here, pixel selection is automated and SEBAL implemented using global climate grids and satellite imagery. SEBAL is compared with the MOD16 algorithm, which uses remotely sensed data on vegetation condition to constrain reference ET from the Penman-Monteith equation. The difference between the evaporative fraction (Λ, range 0-1) from SEBAL and six eddy flux correlation towers was less than 0.10 for three of six towers, and less than 0.24 for all towers. SEBAL ET was moderately sensitive to surface roughness length and implementation over regions smaller than ∼10,000 km2 provided lower error than larger regions. Pixel selection based on TR had similar errors as those based on a vegetation index. For maize, MOD16 had lower error in mean seasonal evaporative fraction (-0.02) compared to SEBAL Λ (0.23), but MOD16 significantly underestimated the evaporative fraction from rice (-0.52) and cotton fields (-0.67) compared with SEBAL (-0.09 rice, -0.09 cotton). MOD16 had the largest error over short crops in the early growing season when vegetation cover was low but land surface was wet. Temperature-based methods like SEBAL can be automated and likely outperform vegetation-based methods in irrigated areas, especially under conditions of low vegetation cover and high soil evaporation. This article is protected by copyright. All rights reserved.
The study analysed water use productivity among smallholder homestead food gardening and irrigation crop farmers in the North West province, South Africa. Home gardening and irrigation constitute the most important rural development investment strategies that can have direct impact on poverty and food security. Using a large sample size technique of n>30, 160 gardeners were selected for the study. Data was collected using a structured questionnaire and subjected to analysis using SPSS. Frequency counts and percentages were used to describe demographics. Multiple regressions were also used to identify determinants.
The independent variables were significantly related with an F value of 3.074, P < .05. Also, an R value of 0.506 showed that there is a strong correlation between socio-economic characteristics and water use productivity. The results further predicted R Square 26% of the variation in water use productivity. Five out of sixteen were significant, with three variables being significant at 5% (type of crop, social participation and market outlet) while two variables were significant at 10% (home food security and attitude).
Significant determinants of water use productivity were type of cropping (t =-2.443, P =.016), social participation (t =2.599, P = .010), marketing outlets (t = 2.810, P = .006), home food security (t=-1.777, P = .078) and attitude (t = -1.727, P = .086). The results imply that the higher attitude, marketing, home food security, social participation and type of crop, the higher the use of water productivity among farmers. However, insignificant determinants of water use productivity were farming experience (t = 0.571, p=0.569), education (t = -1.048, p = 0.296), land ownership (t = -1.416, p = 0.159) and age (t = -0.782, p = 0.436). The results imply that the lower the farming experience, education skill, land ownership and age, the lower the water productivity use among farmers.
In the current scenario of worldwide limited water supplies, conserving water is a major concern in agricultural areas. Characterizing within-orchard spatial heterogeneity in water requirements would assist in improving irrigation water use efficiency and conserve water. The crop water stress index (CWSI) has been successfully used as a crop water status indicator in several fruit tree species. In this study, the CWSI was developed in three Prunus persica L. cultivars at different phenological stages of the 2012 to 2014 growing seasons, using canopy temperature measurements of well-watered trees. The CWSI was then remotely estimated using high-resolution thermal imagery acquired from an airborne platform and related to leaf water potential (ѰL) throughout the season. The feasibility of mapping within-orchard spatial variability of ѰL from thermal imagery was also explored. Results indicated that CWSI can be calculated using a common non-water-stressed baseline (NWSB), upper and lower limits for the entire growing season and for the three studied cultivars. Nevertheless, a phenological effect was detected in the CWSI vs. ѰL relationships. For a specific given CWSI value, ѰL was more negative as the crop developed. This different seasonal response followed the same trend for the three studied cultivars. The approach presented in this study demonstrated that CWSI is a feasible method to assess the spatial variability of tree water status in heterogeneous orchards, and to derive ѰL maps throughout a complete growing season. A sensitivity analysis of varying pixel size showed that a pixel size of 0.8 m or less was needed for precise ѰL mapping of peach and nectarine orchards with a tree crown area between 3.0 to 5.0 m2.
This rigorous yet accessible text introduces the key physical and biochemical processes involved in plant interactions with the aerial environment. It is designed to make the more numerical aspects of the subject accessible to plant and environmental science students, and will also provide a valuable reference source to practitioners and researchers in the field. The third edition of this widely recognised text has been completely revised and updated to take account of key developments in the field. Approximately half of the references are new to this edition and relevant online resources are also incorporated for the first time. The recent proliferation of molecular and genetic research on plants is related to whole plant responses, showing how these new approaches can advance our understanding of the biophysical interactions between plants and the atmosphere. Remote sensing technologies and their applications in the study of plant function are also covered in greater detail.
Water is a crucial element for plant growth, metabolic processes, and general health. Water-deficit, typically simplified by drought stress, is the most critical photosynthetic source of stress that restricts plant growth, crop yield, and food product quality. This research highlights state of the art, possibilities for detecting the canopy temperature by integrating very-high-resolution RGB and thermal imagery from UAV. A multi-rotor drone has assembled by DJI (S900) attached with RGB thermal and cameras was used for experiments. The thermal cameras have a spectral range of 7.5-13μm, a resolution of 640 × 512 pixels, thermal sensitivity of <0.05 °C at ＋30 °C, and a focal length of 25 mm, respectively. UAV flights were operated with DJI ground stations pro software (SZ DJI Technology Co. Ltd., China), using the DJI A3 flight controller. This work aimed to compare the accuracy of canopy temperature and to evaluate the performance of thermography. The extracted CT results were closely related to ground truth CT with the value of (R²) 0.9297 and correlation (r) 0.97702, respectively. The calculated results of CWSI showed a strong relation with gs under different irrigation levels 90%–100%, 75%, 60%, and 50% of the field capacity. The relationship of each time of day was substantial with (R²) 0.90, 0.75, and 0.86, respectively. The Correlation coefficients (R²) of CT, stomatal conductance, and SD were compared and found to be 0.755, 0.67, and 0.695, respectively. Results stated that this approach estimates the most reliable temperature around 35 °C to 40 °C. This study demonstrates the different temperature based spectral indices and provides accurate, rapid, and reliable canopy temperature quantification.
The several changes happening in environmental, social, economic, technological and geopolitical spheres of our society result in countless risks, challenges and opportunities for human development. The global population crosses 7.7 billion with the loss of biodiversity, increasing pressure on food, water, and energy resources. The migration of people from rural to urban in large scale is a matter of concern; as the global urban population will almost reach 68% by 2050, approximately 6 billion. A very high concentration of people living in urban areas and growth projection pose a serious challenge for large cities for vulnerability. Amongst the several challenges, food security will be a serious issue for the future of cities. In this context, the Fourth Industrial Revolution and movement towards creating Smart Cities have to provide solutions and opportunities to deal with those challenges. In this opinion paper, we seek to discuss the future of cities, with a holistic vision of several actions to deal with food security challenges in urban centers.
The scarcity of clean water resources around the globe has generated a need for their optimum utilization. Internet of Things (IoT) solutions, based on the application specific sensors’ data acquisition and intelligent processing, are bridging the gaps between the cyber and physical worlds. IoT based smart irrigation management systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet. The sensing nodes, involved in the ground and environmental sensing, consider soil moisture, soil temperature, air temperature, Ultraviolet (UV) light radiation, and relative humidity of the crop field. The intelligence of the proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web-services and a web-based information visualization and decision support system provides the real-time information insights based on the analysis of sensors data and weather forecast data. The system has a provision for a closed-loop control of the water supply to realize a fully autonomous irrigation scheme. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging.