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Use of mobile applications in smart agriculture
Abstract
This document provides information on the use of information and communication technology (ICT) in agriculture and agriculture educational training (AET). It addresses the use of robotics, drones, mobile applications, and GIS with practical illustrations of applications and their benefits.
Agriculture has a significant role in cultural life, and Agriculture has a significant role in the economies of many countries. To come up with the best possible conclusion from this study. It's essential to pay attention to critical factors, including energy, water availability, labour, and a correct watering plan for crops. Researchers in this study were interested in building a smartphone application that would allow farmers to operate an IoT-based automated irrigation system remotely. Paddy field photos were used to create a deep learning model called Paddy Field Radial Basis Function Networks (PF-RBFNs). The model tells the farmer how much water will be needed in a certain field area for irrigation. A real-time picture dataset and a raspberry pi-based hardware model were used to test this approach. The model was compared to three different deep learning models: LSTMs, RNNs, and GANs. It was found that this proposed PF-RBFNs model has a 93% accuracy rate.
Agriculture in India is at a fork in the road, and the introduction of modern ICT solutions can significantly enhance crop productivity and profitability, resulting in benefits for smallholder farmers in India, where agriculture contributes for over 17% of the nation's Gross Domestic Product. In terms of agricultural modernization, the country whose GDP is based on agriculture is incomplete. High labor costs, crop production uncertainty, lack of education and information about new methods and technology, continuing to the same traditional practices in agriculture, and inefficient irrigation system use are all factors for low productivity and crop drying may occur as a result of this unpredictability in the cultivation process. Digital India would have used the internet and mobile applications to interconnect rural Indian farmers everywhere in the world and providing them with all the resources they need to succeed in agriculture in India. The research examined at how ICT and Android applications are implemented in farming today, and the way they have revolutionized business in agriculture by bring forth a computerized platform, along with their influence in the business. This paper focuses the concept of smart farming, which utilizes mobile application technologies to support farmers with agricultural production information such as seed expenditures, temperature and humidity, composition of the soil, humidity level, weather forecast, pesticides and fertilizers utilization, knowledge of the various government schemes and facilities for farmers, and many more. This paper will also help in bringing out the current agricultural difficulties that farmers are facing. According to the survey, there are only a few agricultural based applications available in India. That provides reliable information to the farmers. This research paper focuses on the comparative analysis of various Mobile applications that supports the farmers in their various agricultural activities. Number of features the applications like their precision level, data reliability, certificated contents availability and also the services for specific geographical locations is compared. So as to propose an application with better features and better farming support. There are a number of mobile applications available to improve the current state of farming; a summary of all applications is provided below.KeywordsAgriculture in IndiaSmart farming technologiesMobile applicationsInformation
This paper presents a financially viable and non-destructive rail-based video monitoring method that utilizes optical image segmentation to estimate the canopy leaf area index (LAI) of greenhouse tomato plants. The LAI is directly related to the time-dependent crop growth and indicates plant health and potential crop yields. A rail-guided mobile camera system was commissioned that records continuous images by scanning multiple rows of two tomato plant species for over two years. UNET semantic image segmentation of the individual image frames was performed to compute the relative leaf area over time. This study also describes the image annotation process necessary to train the neural network and evaluate the segmentation results. The results are calibrated and compared to the defoliation-based (destructive) LAI estimation performed by the grower. This UNET segmentation performs well, which is enabled through the controlled environment and the well-defined boundary conditions provided by the greenhouse environment and the managed measurement conditions. Our results deviate from the manual LAI estimation by less than ten percent. Further, we are able to minimize confusion between foreground and background plants and other obstructions with an estimated error smaller than three percent, which is strictly necessary to produce reproducible results.
Leaf area in plants is critical for the photosynthesis process. Thus, quantification of foliar area needed for the photosynthetic process is impartment. The recent methods used to quantify the foliar damage
are relatively expensive and are mainly dependent on complex and state of art devices and instruments. Instead, smartphone application may be a good source to measure the foliar damage. In this study, foliar damage of Spodoptera litura Fab. (Lepidoptera:Noctuidae) on sunflower and soybean crops was measured using BioLeaf android application. The infected leaves due to citrus canker were also tested
using BioLeaf for foliar analysis. Our results demonstrated that the quantification of foliar damage using BioLeaf application was easy and accurate. This method is also comparable with other techniques like digital analysis by ImageJ software and leaf area meter devices. With these results, it is concluded that the BioLeaf application has resulted in an accurate leaf area measuring tool of foliar damage with minimum time and cost and can be used in various crops for damage assessment.
Featured Application
A low complexity image processing and a new classification method are proposed in this paper. These methods can be employed by plant disease diagnosis applications implemented on smart phones. The supported set of diseases can be extended by the end user.
Abstract
A plant disease diagnosis method that can be implemented with the resources of a mobile phone application, that does not have to be connected to a remote server, is presented and evaluated on citrus diseases. It can be used both by amateur gardeners and by professional agriculturists for early detection of diseases. The features used are extracted from photographs of plant parts like leaves or fruits and include the color, the relative area and the number of the lesion spots. These classification features, along with additional information like weather metadata, form disease signatures that can be easily defined by the end user (e.g., an agronomist). These signatures are based on the statistical processing of a small number of representative training photographs. The extracted features of a test photograph are compared against the disease signatures in order to select the most likely disease. An important advantage of the proposed approach is that the diagnosis does not depend on the orientation, the scale or the resolution of the photograph. The experiments have been conducted under several light exposure conditions. The accuracy was experimentally measured between 70% and 99%. An acceptable accuracy higher than 90% can be achieved in most of the cases since the lesion spots can recognized interactively with high precision.
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
Soil sampling is an important tool to gather information for making proper decisions regarding the fertilization of fields. Depending on the national regulations, the minimum frequency may be once per five years and spatially every ten hectares. For precision farming purposes, this is not sufficient. In precision farming, the challenge is to collect the samples from such regions that are internally consistent while limiting the number of samples required. For this purpose, management zones are used to divide the field into smaller regions. This article presents a novel approach to automatically determine the locations for soil samples based on a soil map created from drone imaging after ploughing, and a wearable augmented reality technology to guide the user to the generated sample points. Finally, the article presents the results of a demonstration carried out in southern Finland.
Land evaluation is a foundation for sustainable land resource planning and management. In Ethiopia, national economic development is highly dependent on agriculture. And, the productivity of the land is low as a result of land degradation due to mismanagement of the land and intensive cultivation; this situation is worse in Ethiopian Highlands like Andit Tid watershed. Therefore, matching crop requirement with resource available through land suitability analysis has become an urgent need to sustain the productivity of agricultural land in the study area. This study aimed to address land suitability analysis by using geographical information system-based multi-criteria approach in Andit Tid watershed. The analysis of land suitability for major crops (wheat and barley) revealed that, though, there are slight variation in suitable classes for each crop, most part of the watershed was moderately suitable for both crops with 266.55 ha (77.42%) and only very small part of the land 2.43 ha (0.51%) was highly suitable for both crops. On the other hand, 9.3 ha (2%) and 3.6 ha (0.77%) of the land was marginally suitable and not suitable for both wheat and barley crops, respectively. The main limiting factors for land suitability were soil depth, texture, temperature, slope and erosion hazard. The spatial distribution showed that most of the cultivation currently practised in marginally suitable land in the lower part of the watershed. Most of the land parcel being used against its suitability potential in study area. Thus, land-use pattern needs to be modified based on its suitability potential.
Remote sensing and Geographical Information System (GIS) offers an abundant opportunity to monitor and manage natural resources at multi-temporal, multi-spectral and multi-spatial resolution. It is an urgent need to understand the specialized capabilities of an ever-expanding array of image sources and analysis techniques for natural resource managers. In this review, we compile the various applications of remote sensing and GIS tools that can be used for natural resource management (agriculture, water, forest, soil, natural hazards). The information is useful for the natural resource managers to understand and more effectively collaborate with remote sensing scientists to develop and apply remote sensing science to achieve monitoring objectives
Decision Support Systems (DSS) provide a framework for integration, management, analysis and finally graphical presentation of a particular phenomena in order to improve the existing decision making process. In the present day context, the decision support system concept has been extended to the spatial dimension by integrating GIS and DSS into spatial decision support systems (SDSS). Due to the lack of computer software to develop user friendly interfaces in the past, GIS have not been used as part of SDSS. Instead GIS have been used to generate and store spatial data which were used that time as inputs for the analytical or manual statistical models. GIS was used independently to display maps by inputting results of the analytical models. With the passing time much of the research has been done on the use of GIS in the visualisation of the results of the analytical models. Developing user friendly graphical interfaces in incorporating analytical models into GIS to arrive at SDSS's is one of the active areas in modern day agricultural management system. Technological or precision farming, a combination of GIS,GPS receivers, continuous yield sensors, geostatistics and variable rate applicators is an innovative approach to practice of sustainable agriculture. The other SDSS applications discussed in this paper are on watershed management, crop productivity management and policy decision analysis.
The need for higher agricultural productivity has demanded the intensive use of pesticides. However, their correct use depends on assessment methods that can accurately predict how well the pesticides' spraying covered the intended crop region. Some methods have been proposed in the literature, but their high cost and low portability harm their widespread use. This paper proposes and experimentally evaluates a new methodology based on the use of a smartphone-based mobile application, named DropLeaf. Experiments performed using DropLeaf showed that, in addition to its versatility, it can predict with high accuracy the pesticide spraying. DropLeaf is a five-fold image-processing methodology based on: (i) color space conversion; (ii) threshold noise removal; (iii) convolutional operations of dilation and erosion; (iv) detection of contour markers in the water-sensitive card; and, (v) identification of droplets via the marker-controlled watershed transformation. The authors performed successful experiments over two case studies, the first using a set of synthetic cards and the second using a real-world crop. The proposed tool can be broadly used by farmers equipped with conventional mobile phones, improving the use of pesticides with health, environmental and financial benefits.
This paper discusses a case study of designing a private Internet of Things (IoT) enabled platform for the research in precision agriculture and ecological monitoring domains. The system architecture is gradually derived using an approach of multiple, concurrent views. Each view represents an architectural perspective describing the solution from the viewpoint of different stakeholders, such as end-users, researchers, developers,
and project managers. The end-user requirements have been identified using a set of high-level scenarios,
which capture the context and illustrate the motivation for building the platform. The requirements and architecture of the proposed platform have been derived so that the users of the platform, researchers, and developers on the project, can utilize it for prototyping solutions for these high level use cases. The paper further describes the implementation of the platform and its evaluation using various sensor nodes deployed at the research and end-user facilities. The solution is open to further development with respect to supporting additional IoT protocols, data types, and interfacing to various analytics tools. The proposed architecture can also be implemented using different server platforms and cloud technologies.
We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.
This paper examines the ethics of big data in agriculture, focusing on the power asymmetry between farmers and large agribusinesses like Monsanto. Following the recent purchase of Climate Corp., Monsanto is currently the most prominent biotech agribusiness to buy into big data. With wireless sensors on tractors monitoring or dictating every decision a farmer makes, Monsanto can now aggregate large quantities of previously proprietary farming data, enabling a privileged position with unique insights on a field-by-field basis into a third or more of the US farmland. This power asymmetry may be rebalanced through open-sourced data, and publicly-funded data analytic tools which rival Climate Corp. in complexity and innovation for use in the public domain.
Agronomy is a branch of agriculture that deals with soil and crop. Soil varies in space and is responsible for variation in the growth and yield of crops on the field. This variation in the yields of crops planted and monitored on the same parcel of land under the same environmental conditions has been a great concern to farmers. Spatial variations of soil nutrients status, as caused by topography, soil texture and manage‐ ment practices, have been observed across the fields. Hence, the need to separate the field into site specific management units using geographical information systems (GIS) for effective soil and crop management in order to obtain optimum productivity. Over the years, field sizes, farming direction, locations of fences, rotations and fertility programmes have changed the nutritional status of the farms. Consequently, the productivity of the soil has equally been affected. In spite of these factors, convention‐ al agriculture treats an entire field uniformly with respect to the application of fertiliser, pesticides, soil amendments and other chemical application. The use of GIS will help farmers to overcome over-or under-applications of fertiliser and other agrochemical applications. The potential of GIS application in agronomy is obviously large. However, the GIS user community in the field of agronomy is rather small compared to other business sectors. To advance the use of GIS in agronomic studies, this Chapter in book tends to explore the applications of GIS to some fields in agronomy.
Farming is undergoing a digital revolution. Our existing review of current Big Data applications in the agri-food sector has revealed several collection and analytics tools that may have implications for relationships of power between players in the food system (e.g. between farmers and large corporations). For example, Who retains ownership of the data generated by applications like Monsanto Corproation's Weed I.D. “app”? Are there privacy implications with the data gathered by John Deere's precision agricultural equipment? Systematically tracing the digital revolution in agriculture, and charting the affordances as well as the limitations of Big Data applied to food and agriculture, should be a broad research goal for Big Data scholarship. Such a goal brings data scholarship into conversation with food studies and it allows for a focus on the material consequences of big data in society.
Machine vision technologies hold the promise of enabling rapid and accurate fruit crop yield predictions in the field. The key to fulfilling this promise is accurate segmentation and detection of fruit in images of tree canopies. This paper proposes two new methods for automated counting of fruit in images of mango tree canopies, one using texture-based dense segmentation and one using shape-based fruit detection, and compares the use of these methods relative to existing techniques:—(i) a method based on K-nearest neighbour pixel classification and contour segmentation, and (ii) a method based on super-pixel over-segmentation and classification using support vector machines. The robustness of each algorithm was tested on multiple sets of images of mango trees acquired over a period of 3 years. These image sets were acquired under varying conditions (light and exposure), distance to the tree, average number of fruit on the tree, orchard and season. For images collected under the same conditions as the calibration images, estimated fruit numbers were within 16 % of actual fruit numbers, and the F1 measure of detection performance was above 0.68 for these methods. Results were poorer when models were used for estimating fruit numbers in trees of different canopy shape and when different imaging conditions were used. For fruit-background segmentation, K-nearest neighbour pixel classification based on colour and smoothness or pixel classification based on super-pixel over-segmentation, clustering of dense scale invariant feature transform features into visual words and bag-of-visual-word super-pixel classification using support vector machines was more effective than simple contrast and colour based segmentation. Pixel classification was best followed by fruit detection using an elliptical shape model or blob detection using colour filtering and morphological image processing techniques. Method results were also compared using precision–recall plots. Imaging at night under artificial illumination with careful attention to maintaining constant illumination conditions is highly recommended.
Irrigation is an essential component of crop production to meet retailer demands for premium quality when rainfall is insufficient. Under drought conditions, irrigation can be constrained by water resources availability, with consequent impacts on yield, quality and revenue. Whilst most agriculture in Europe is rainfed, greater dependence on supplemental irrigation could become more important in humid environments due to a changing climate with greater rainfall uncertainty and higher frequency of droughts. By combining industry and farm level economic data, with geospatial information on agricultural land use, agroclimate, soils and irrigation practices within a GIS, this paper estimates the total financial benefit of outdoor irrigated production in England and Wales assuming no constraints in resource availability and optimal irrigation practices. The analysis suggests that the total net benefits of irrigation in a ‘design’ dry year are around £665 million, with an average irrigation water productivity in excess of £3.3 per m3 (close to £1.1 per m3 excluding soft fruit). Map outputs highlight significant regional differences in water productivity reflecting the composition of land use and the importance of crop mix in determining economic value. A sensitivity analysis to changes in agroclimate, market conditions (crop prices) and water supply (costs) illustrates how the benefits might change under contrasting scenario. The study highlights the importance of supplemental irrigation, even in a humid climate, and the risks that future droughts and/or constraints in water resource availability might have on agricultural systems, livelihoods and the rural economy. The implications for water resources and drought management are discussed.
Leaf area index (LAI) and plant area index (PAI) are common and important biophysical parameters used to estimate agronomical variables such as canopy growth, light interception and water requirements of plants and trees. LAI can be either measured directly using destructive methods or indirectly using dedicated and expensive instrumentation, both of which require a high level of know-how to operate equipment, handle data and interpret results. Recently, a novel smartphone and tablet PC application, VitiCanopy, has been developed by a group of researchers from the University of Adelaide and the University of Melbourne, to estimate grapevine canopy size (LAI and PAI), canopy porosity, canopy cover and clumping index. VitiCanopy uses the front in-built camera and GPS capabilities of smartphones and tablet PCs to automatically implement image analysis algorithms on upward-looking digital images of canopies and calculates relevant canopy architecture parameters. Results from the use of VitiCanopy on grapevines correlated well with traditional methods to measure/estimate LAI and PAI. Like other indirect methods, VitiCanopy does not distinguish between leaf and non-leaf material but it was demonstrated that the non-leaf material could be extracted from the results, if needed, to increase accuracy. VitiCanopy is an accurate, user-friendly and free alternative to current techniques used by scientists and viticultural practitioners to assess the dynamics of LAI, PAI and canopy architecture in vineyards, and has the potential to be adapted for use on other plants.
India is an agriculture based developing country. Information dissemination to the knowledge intensive agriculture sector is upgraded by mobile-enabled information services and rapid growth of mobile telephony. It bridge the gap between the availability of agricultural input and delivery of agricultural outputs and agriculture infrastructure. Mobile computing, cloud computing, machine learning and soft computing are the immerging techniques which are being used in almost all fields of research. Apart from this, they are also useful in our day-today activities such as education, medical and agriculture. This paper explores how Android Apps of agricultural services have impacted the farmers in their farming activities.
To reduce the human influence on natural resources and to identify an appropriate land use, it is essential to carry out scientific land evaluations. Such kind of analysis allows identifying the main limiting factors for the agricultural production and enables decision makers to develop crop managements able to increase the land productivity. Objectives of this study were to develop a GIS based approach for land use suitability assessment which will assist land managers and land use planners to identify areas with physical constraints for a range of nominated land uses. Georeferenced soil survey data and field work observations have been integrated in a GIS based land use suitability assessment for agricultural planning in Chamarajanagar district, Karnataka, India. Also, GIS has been used to match the suitability for main crops based on the requirements of the crops and the quality and characteristics of land. Different land quality parameters, viz. soil texture, depth, erosion, slope, flooding and coarse fragments under various land units were evaluated for the crops. Subsequently all of them were integrated using a sequence of logical operations to generate land suitability and capability maps. Suitability and capability maps for each land use were developed to illustrate these suitability degrees and display the spatial representation of soils suitable for agriculture. It was also found that better land use options could be implemented in different land units as the conventional land evaluation methods suffer from limitation of spatial analysis for the suitability of various crops.
The information age of the 1990s is a global consciousness where scientific and technological advances are assumed capable of solving global environmental problem. A significant characteristic of nonpoint source (NPS) pollution problems is the lack of regard for political boundaries and physical barriers between cities, states, nations, and continents. The widespread nature of such environmental problems often results in an analogous diffuse acceptance of responsibility for resolution. Thus, an ability to accurately assess the present and future impact of human activities on the global ecosystem would provide a most powerful basis for environmental stewardship and guiding future human actions. To responsibly respond to impaired ecosystem functioning (i.e., with respect to such issues as climatic change, stratospheric ozone depletion, species diversification, erosion, deforestations, desertification, agricultural sustainability, and nonpoint source pollution), it is necessary to examine these issues not only from a multidisciplinary systems-based approach, but also with an approach that accounts for spatial and temporal context. The problems and philosophical issues of addressing NPS pollution is the vadose zone within a spatial and temporal context are presented.
Geographic Information Systems (GIS) and Remote Sensing (RS) have become globally important tools in modern precision agriculture. In Bangladesh, GIS and RS technology have been utilized since 1991 in several areas of agriculture. The areas include (i) development of inclusive GIS-based Land Resources Information System (LRIS) founded on Agroecological Zone (AEZ) database system, (ii) agricultural resource information system based on soil characteristics, hydrology, individual crop attributes, inputs and management levels considering climatic conditions, and seasonal variability, and (iii) development of Soil and Land Resource Information System (SOLARIS) for managing geo-spatial data base developed by Soil Resource Development Institute (SRDI). Besides, Bangladesh Space Research and Remote Sensing Organization (SPARRSO) have been using RS and GIS in limited scale for the last two decades in major crop area monitoring. RS and GIS have limited use in other areas like coastal zone monitoring, drought and waterlogging stress monitoring as well as ground water monitoring. Other important areas need to be addressed properly by using the advancement of GIS and RS are (i) 12% hilly land areas vulnerable to soil erosion and deforestation and (ii) a vast coastal land exposed to soil salinity. For this purpose, there is a need of sufficient reliable and accurate temporal remotely sensed and digital database coverage along with the adequate coordination among the government departments, research organizations and educational institutions for data updating, data gathering and data analysis, judicious resource planning and projection. Strengthening of academic curricula incorporating updated RS and GIS knowledge and technique into higher studies of the country is also needed.
Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error (RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.
Smart phone technology creates new opportunities for farm management applications in small farms. Farmers working on small farms are now able with a low cost smart phone and the specialized software (in our case FarmManager) to obtain facilities that couldn't have on their hands before. The use of the FarmManager software in a smart phone can overleap the high difficulties of farm management requirements which were stand as obstacle for many years so far. Tasks such as field definition, task operations, lists and reports and all farming use data can be submitted and carried on together in a smart phone at any farm working condition. In this paper we present the FarmManager which is an Android smart phone application and how it creates the management base for recording and browsing of ground fields, field relations (occupied or rented land), cultivation and its tasks, equipment, employees and European cultivations reports and all of them to be performed by the touch of smart phone screen button. The use of software is currently freely available and there are more than one thousand farmers using it in Greece.
Agriculture is now a trillion-dollar industry, making significant contributions to the growth of several developing as well as developed countries. The huge rise in the growing demand of food and making it sustainable for people is encouraging the need for smart farming. There is a great potential to transform traditional farming profoundly by integrating Internet of things (IoT), Blockchain, and Geospatial technologies to emerge as Smart Farming. Blockchain based farming provides farmers various instant agricultural data at one secured platform, represents a unique opportunity to bring greater efficiency, sustainable crop production, tackle food scarcity, and adds transparency and traceability to the exchange of data related to farming management. Uses of Blockchain in farming management is not only improving the food traceability but also making farming safer for farmers as well as consumers involved, less uncertain and more profitable to the farmers. This paper describes the use of Blockchain technology in farming to manage the practices in a smarter as well as sustainable way, by presenting the decentralized infrastructure with added immutable geospatial technology and IoT sensors capabilities. Our proposed system architecture will explain how Blockchain technology with GIS & IoT will revolutionize the traditional farming practices. Moreover, Blockchain preserves the stakeholders privacy by enhancing IoT framework with more reliable and secure data. Likewise, geospatial technologies create the greater impact by providing visualization and decision making through analytics by transforming traditional farming into sustainable farming.
Over the last few years, an extensive set of technologies have been systematically included in precision agriculture (PA) and also in precision viticulture (PV) practices, as tools that allow efficient monitoring of nearly any parameter to achieve sustainable crop management practices and to increase both crop yield and quality. However, many technologies and standards are not yet included on those practices. Therefore, potential benefits that may result from putting together agronomic knowledge with electronics and computer technologies are still not fully accomplished.
Both emergent and established paradigms, such as the Internet of Everything (IoE), Internet of Things (IoT), cloud and fog computing, together with increasingly cheaper computing technologies – with very low power requirements and a diversity of wireless technologies, available to exchange data with increased efficiency – and intelligent systems, have evolved to a level where it is virtually possible to expeditiously create and deploy any required monitoring solution.
Pushed by all of these technological trends and recent developments, data integration has emerged as the layer between crops and knowledge needed to efficiently manage it. In this paper, the mySense environment is presented, aimed to systematize data acquisition procedures to address common PA/PV issues. mySense builds over a 4-layer technological structure: sensor and sensor nodes, crop field and sensor networks, cloud services and support to front-end applications. It makes available a set of free tools based on the Do-It-Yourself (DIY) concept and enables the use of Arduino® and Raspberry Pi (RPi) low-cost platforms to quickly prototype a complete monitoring application. Field experiments provide compelling evidences that mySense environment represents an important step forward towards Smart Farming, by enabling the use of low-cost, fast deployment, integrated and transparent technologies to increase PA/PV monitoring applications adoption.
While recent increased demand on global food supply has led to improved economic returns for the agricultural industry, there has also been an increase in risk associated with food production systems because input costs have also increased signicantly.1 To lower risk, increase energy efciency, enhance productivity, and improve protability, agricultural producers are increasingly turning to the use of IT to aid their decision-making processes.2 In addition to land, labor, and capital, which have long been agriculture’s traditional assets, information management has become the fourth asset,3 of increasing importance and has come to be known as precision agriculture. According to the National Research Council, “precision agriculture is a management strategy that uses information technologies to bring data from multiple sources to bear on decisions associated with crop production.”
Environmental applications have long been a core use of GIS. However, the effectiveness of GIS-based methods depends on the decision-making frameworks and contexts within which they are employed. GIS for Environmental Decision-Making takes an interdisciplinary look at the capacities of GIS to integrate, analyze, and display data on which decisions must be based. It provides a broad prospective on the current state of GIS for environmental decision-making and emphasizes the importance of matters related to data, analysis, and modeling tools, as well as stakeholder participation. The book is divided into three sections, which effectively relate to three key aspects of the decision-making process as supported by GIS: data required, tools being developed, and aspects of participation. The first section stresses the ability to integrate data from different sources as a defining characteristic of GIS and illustrates the benefits that this can bring in the context of deriving land-use and other information. The second section discusses a range of issues concerning the use of GIS for suitability mapping and strategic planning exercises, through illustrative examples. The last section of the book focuses on the use of GIS-based techniques to facilitate public participation in decision-making processes. In particular, it provides an overview of developments in this area, concentrating on how GIS, modeling, and 3D landscape visualization techniques are gradually achieving closer integration. Given the complex challenges presented by global environmental change, GIS for Environmental Decision-Making provides a clear illustration of how the use of GIS can make significant contributions to trans-disciplinary initiatives to address environmental problems.
GIS can be a useful tool for spatial or land-use planning, but only if several conditions are fulfilled. The key conditions are related to 1) the quality of basic spatial information, and 2) the statistical methods applied to the spatial nature of the data. Appropriate information and methods allow the generation of robust models that guarantee objective and methodologically sound decisions. In this study we apply several multivariate statistical methods and test their usefulness to provide robust solutions in forestry planning using GIS. We must emphasize that in our Iberian study area, where forests have progressively decreased in extent over centuries, the main aims of forestry planning are the reduction of forest fragmentation, biodiversity conservation, and restoration of degraded biotopes. The research develops a set of likelihood or suitability models for the presence of tree species that are widely distributed over a study area of 41,000 km2. The utility of suitability models has been demonstrated in some previous studies1, but they are still not as widely employed as might be expected. A suitability model is a raster map in which each pixel is assigned a value reflecting suitability for a given use (e.g., presence of a tree species). Suitability models can be generated through diverse techniques, such as logistic regression or non-parametric CART (classification and regression trees) and MARS (multiple adaptive regression splines)2-4. All of these techniques require a vegetation map (dependent variable) and a set of environmental variables (climate, topography, geology, etc.) which potentially influence the vegetation distribution. The foundation of the method is to establish relationships between the environmental variables and the spatial distribution of the vegetation. Typically, each vegetation type will respond in a different way as a consequence of its contrasting environmental requirements.
The world population is estimated to reach nine billion by 2050. Many challenges are adding pressure on the current agriculture supply chains that include shrinking land sizes, ever increasing demand for natural resources and environmental issues. The agriculture systems need a major transformation from the traditional practices to precision agriculture or smart farming practices to overcome these challenges. Geographic information system (GIS) is one such technology that pushes the current methods to precision agriculture. In this paper, we present a systematic literature review (SLR) of 120 research papers on various applications of big GIS analytics (BGA) in agriculture. The selected papers are classified into two broad categories; the level of analytics and GIS applications in agriculture. The GIS applications viz., land suitability, site search and selection, resource allocation, impact assessment, land allocation, and knowledge-based systems are considered in this study. The outcome of this study is a proposed BGA framework for agriculture supply chain. This framework identifies big data ana-lytics to play a significant role in improving the quality of GIS application in agriculture and provides the researchers, practitioners, and policymakers with guidelines on the successful management of big GIS data for improved agricultural productivity.
Land consumption is a good indicator to directly diagnose present and future imbalances in territories, and indirectly, possible issues associated to the management of other resources. Therefore, after reaching the target of standardizing urban research that makes it possible to build healthier and greener cities, the real challenge for the future is to make the leap from urban scale to regional scale and deploy these policies in an integrated manner, in so-called "smart territories".
In that context, this paper presents a model of multidisciplinary analysis through indicators based in land consumption and transformation rates. The model, called GIS-LiDAR retrospective analysis, is implemented through territorial information tools in order to simulate and diagnose possible future imbalances based on past and current trends. This innovative methodology will be applied in a Spanish Mediterranean coastal area called the Campo de Cartagena, a territory with issues related to low-density urban sprawl, intensive agriculture and mass tourism coastal urbanization. This territory of high economic activity and with important environmental protected areas like the Mar Menor lagoon as well as complex interrelated phenomena will be "retrohistorically" diagnosed from the perspective of land transformation over 60 years. The method, designed to advance future scenarios and help planners in decision-making, will show dangerous current trends leading to imbalances in this area so that future planning can be implemented with smart (sustainable) criteria.
Agriculture in northern Ontario, Canada, has not yet reached the level of development of the southern regions of the province. In spite of the increasing desirability of the former region for agricultural expansion, northern agricultural producers-as well as other producers in "emerging" areas-have less access to information and decision support services relative to more established agricultural regions. At the same time, geographic information systems (GIS) are now being integrated into precision agriculture to assess field variability, to ensure optimal use of information, to maximize output, and to increase efficiency. To address this trend, a community-based research initiative based on an interactive web-based information visualization and GIS decision support system has been deployed with the aim of providing northern Ontario producers with access to the data they need to make the best possible decisions concerning their crops. This system employs citizen science and community-based participatory research to build a mutually beneficial partnership between agricultural producers , researchers, and other community stakeholders.
With a growing worldwide population, water scarcity is growing as well. As most of the usable water on Earth is used for agriculture, micro-irrigation is considered to be desirable, sustainable and much more efficient method than conventional irrigation. This article focuses on management of water resources and performance evaluation of micro-irrigation firms. The competition and corporate financial performances is evaluated using APC model in order to provide strategic directions in water industry. The results, from application of APC model to a micro-irrigation company, provide insight to management in taking corrective actions for improvement in the short term. The results also provide cues in conducting SWOT analysis for developing strategies to compete effectively in the long term. This paper is important to researchers and business executives interested in measuring and evaluating corporate performance in terms of productivity, price recovery, and profitability, as well as to those interested in water conservation and sustainability.
Population growth, climate sensitivity, and edaphic properties are important factors that influence decision making and risk mitigation for agricultural production. Within the agricultural sector in Malawi, continuous cropping without the use of long-term sustainable strategies and frequent cultivation on marginal lands have resulted in continually declining soil fertility. Improving soil quality of marginal lands using innovative technologies is imperative for increasing agricultural productivity and improving food security. Here, we propose an ensemble approach to mapping agricultural land suitability and identify the distribution of marginal land in Malawi. Quantitative data available for eight soil and terrain factors were rated individually, and five distinct models were applied to generate a spatial distribution map of land suitability. The results indicate that highly suitable, moderately suitable, marginally suitable, and unsuitable agricultural areas account for 8.2%, 24.1%, 28.0% and 39.7% of the total land area, respectively. The majority of suitable lands are currently used for agriculture, but more than half (57.4%) of Malawi's total cropland exists on marginally suitable or unsuitable land categories, and are likely candidates for rehabilitation through sustainable agricultural practices. The methods and products herein will be valuable resources for effectively managing and improving Malawi's agricultural lands for increasing food security.
More than ever companies are challenged to rethink their offerings while simultaneously being provided with a unique opportunity for creating or recreating their product-service systems. This paper seeks to address how servitisation can utilize the third wave of Internet development, referred to as the Internet of Things (IoT), which may unlock the potential for innovative product-service systems on an unprecedented scale. By providing an analysis of this technological breakthrough and the literature on servitisation, these concepts are combined to address the question of how organizations offering product-service systems can reap the benefits that the IoT. An analysis of three successful IoT implementation cases in manufacturing companies, representing different industry sectors such as metal processing, power generation and distribution, is provided. The results of the empirical research presented in the paper provide an insight into different ways of creating value in servitisation. The paper also proposes a framework that is aimed at proving a better understanding of how companies can create value, and add it to their servitisation processes with, the data obtained by the IoT based solutions. From the value chain perspective, IoT aided servitisation enables organizations to extend their value chains in order better serve their customers which, in turn, might result in increased profitability. The article proposes further research avenues, and offers valuable insight for practitioners.
Agriculture is the backbone of people’s livelihood in Nepal. However, a majority of farmers are constrained to
adopt subsistence agriculture and use traditional farm equipment due to varying topography, small and fragmented farm
holdings, and lack of investment and infrastructure. This study aims to determine the status and potential of agricultural
mechanization in Sunsari district of Nepal through the statistical analysis of data obtained from the field survey and personal
interviews of local farmers. The agricultural mechanization in Sunsari district was found to be at a rudimentary stage
without some of the typical equipment like power tillers, seeders, trans-planters, weeders, and crop harvesters. Major power
sources in the district were found to be human labor, draft animals, and tractors; the latter was used only for land preparation
and transportation. Tractors seemed to reduce the labor utilization, especially labor use for land preparation but
they did not show much influence on crop yield and cropping intensity. The family income was much higher in tractorowned
farms than bullock farms due to the higher income from off-farm activities. The potential levels of mechanization in
the study area were categorized using geographic information system (GIS) mapping and it was observed that about 96.8%
of the total cultivated area can be covered using tractor, and 1.5% can at least be served with power tillers. In the remaining
1.7% area, neither tractor nor power tiller can operate efficiently.
Soybean is one of the ten greatest crops in the world, answering for billion-dollar businesses every year. This crop suffers from insect herbivory that costs millions from producers. Hence, constant monitoring of the crop foliar damage is necessary to guide the application of insecticides. However, current methods to measure foliar damage are expensive and dependent on laboratory facilities, in some cases, depending on complex devices. To cope with these shortcomings, we introduce an image processing methodology to measure the foliar damage in soybean leaves. We developed a non-destructive imaging method based on two techniques, Otsu segmentation and Bezier curves, to estimate the foliar loss in leaves with or without border damage. We instantiate our methodology in a mobile application named BioLeaf, which is freely distributed for smartphone users. We experimented with real-world leaves collected from a soybean crop in Brazil. Our results demonstrated that BioLeaf achieves foliar damage quantification with precision comparable to that of human specialists. With these results, our proposal might assist soybean producers, reducing the time to measure foliar damage, reducing analytical costs, and defining a commodity application that is applicable not only to soy, but also to different crops such as cotton, bean, potato, coffee, and vegetables.
Food is the basic and compulsory requirement of the human being. It is expected that demand for food crops will double during the next 50 years with limited land and water resources. Production of food requires water. Global estimates indicates that irrigated agriculture sector consumes about 85% of the available water. Further it is expected that in this sector will witness increase water consumption about 20% by the year 2025. The requirement of water increases as the production increases. Proper utilization of water resource is required which is known as scheduling of water for irrigation or irrigation scheduling. Studies carried out in the water deficit area indicated that proper irrigation scheduling may save water and energy up to 35%. The present trend of irrigation has caused 25% depletion in ground water reserve by 2010. Research shows that increasing trend in demand of water in irrigation may lead to total depletion of the ground water table within 50 years. More enhancements required in techniques of irrigation scheduling as the data associated with the estimation of scheduling of water are not sufficient and reliable. An attempt has been made to solve some of the existing problems of data insufficiency and non reliable data can better be solved by the help of remote sensing and GIS. An endeavor has been made to compute the three main parameters influencing the irrigation scheduling namely, crop coefficient, albedo and crop surface temperature through intervention of remote sensing. The approach suggested for the irrigation scheduling using remote sensing and GIS can save irrigation water by 12.5% as compared to the conventional or prevailing approach in study area.
Land use change studies increasingly integrate geographic factors to explain uneven patterns of land abandonment. For mountain areas, biophysical factors, such as altitude, and economic factors, such as distance from core areas of economic and urban development, have been associated with agricultural land abandonment. These interpretations have led to agricultural and land use policies based on compensatory measures to maintain economic activity in mountain regions without much consideration of the intra-regional differences in agricultural land abandonment patterns. This paper argues that such differences are significant and should be taken into account in land use and rural policy design. Based on GIS estimations of land cover change for the 1990–2006 period and regression analysis of socio-economic attributes for 417 mountain municipalities in Provence-Alps-Côte d’Azur in Southern France, our research shows that high altitude grasslands are less likely to be abandoned than those located in lower altitude areas. This result is counter-intuitive given the understanding that remoteness and biophysical constraints are often associated with low land rents, therefore with higher levels of abandonment. Our findings also suggest that grassland abandonment is caused by a combination of both local and regional/global factors. European Union policies for maintaining agricultural activity in marginal areas were not fully effective in reducing grasslands abandonment.
The evaluation of land resources has been greatly facilitated by recent advances in information science and computer technology. We describe an effort to move land evaluation into the information age that resulted in AEGIS, the Agricultural and Environmental Geographical Information System. Although the crop models in the DSSAT are basically one-dimensional, agriculture occurs in time and space. It was a logical step to link DSSAT with a geographic information system. The chapter outlines the evolution of the various versions of AEGIS that culminated in the a version for Windows, AEGIS/WIN. Sample applications of AEGIS are presented in three case studies: the feasibility of small irrigation projects in the Andes of Colombia; the evaluation of alternative cropping systems for former sugarcane land in southern Puerto Rico; and assessment of the possible impact of climate change on regional crop production in the southern USA. The accuracy of output from AEGIS is conditioned by limitations of the crop models and by the quality of soil and weather data and their spatial and temporal variability. Nevertheless, AEGIS is a useful tool for expanding the scope of analysis of the DSSAT from a point to an area. The field of systems-based land evaluation is progressing at a rapid pace, and future versions of AEGIS could be greatly enhanced by taking more advantage of the spatial modeling power of a geographic information system.
Agricultural lands have experienced rapid changes during the last decade. In the absence of preventative crop change policies, these changes can affect multiple ecosystem services simultaneously. The objective of this paper is to describe a web-based spatial decision support system (SDSS), which we call the SmartScape™, that helps policymakers to evaluate the consequence of crop changes on various ecosystem services in agriculture landscapes. This paper specifically provides an overview of a newly developed SDSS architecture that: (1) integrates multiple open-source software tools to build a user-friendly web client; (2) integrates a variety of spatial and temporal data and environmental models, into an interactive environment to allow stakeholders with various interests to build crop change scenarios; and (3) allows stakeholders to evaluate and identify suitable crop change policies by visualizing the tradeoffs among multiple ecosystem services in a timely manner. We demonstrate the utility of this architecture through (4) an analysis of a crop change scenario for an agriculture-dominated landscape in Dane County, Wisconsin, USA. Assessment of participant feedback from potential users (group of stakeholders from Dane County as well as group of scientists who were experts in various disciplines, such as computer science, landscape ecology, agriculture, water quality, soil chemistry and climate change) reveals that this SmartScape™ is an effective tool to show the general consequences of various types of crop changes for developing effective crop changes policy in Wisconsin, USA.
The advent of Wireless Sensor Networks (WSNs) spurred a new direction of research in agricultural and farming domain. In recent times, WSNs are widely applied in various agricultural applications. In this paper, we review the potential WSN applications, and the specific issues and challenges associated with deploying WSNs for improved farming. To focus on the specific requirements, the devices, sensors and communication techniques associated with WSNs in agricultural applications are analyzed comprehensively. We present various case studies to thoroughly explore the existing solutions proposed in the literature in various categories according to their design and implementation related parameters. In this regard, the WSN deployments for various farming applications in the Indian as well as global scenario are surveyed. We highlight the prospects and problems of these solutions, while identifying the factors for improvement and future directions of work using the new age technologies.
Soil survey constitutes a valuable resource inventory linked with the survival of life on the earth. The technological advancements in the field of remote sensing and Geographical Information System have been a boon for such surveys. Present paper describes the role of remote sensing and Geographical Information System (GIS) technologies for mapping and characterizing soils at various scales. The spectral behaviour of soil and its components, which is fundamental to deriving information from remote sensing data, is also discussed with illustrations. Furthermore, the scope of present day remote sensing data for varying levels information generation is also reviewed.
Analyzing the impact of different nutrient management scenarios on energy efficiency, this reference offers powerful techniques to help farmers produce greater abundance at lower costs. Sections cover manure management; water and nutrient management in relation to energy efficiency and ethanol production; matching crops, landscape positions, and nutrient management; and nitrogen and energy efficiency. The book includes a broad range of GIS techniques useful over a range of spatial and temporal scales. An interactive CD is provided to help readers work through examples and data.
This research proposes a new model for the generation of basic soil information maps for precision agriculture based on multitemporal remote sensing data analysis and GIS spatial data modelling. It demonstrates (i) the potential of multitemporal soil pattern analysis (ii) to generate functional soil maps at field scale based on soil reflectance patterns and related soil properties and (iii) how to improve these soil maps based on the identification of static homogenous soil patterns by excluding temporal influences from the developed prediction model. Principal components and per-pixel analyses are used for the separation of static soil pattern from temporal reflectance pattern, influenced by (vital and senescent) vegetation and land management practices. The potential of the proposed algorithm is investigated using multitemporal multispectral RapidEye satellite imagery at a demonstration field “Borrentin” field in Northeast Germany.
This study reveals to identify the changes of land use/land cover of rural agricultural watershed of Tamilnadu. The relationship between Land Use and Land Cover Changes (LULCC) has identified using IRS IC LISS III and PAN merged data. Further, the preparation of LULC map using Survey of India (SOI) Toposheet for the year 1972 contain come up to in multipurpose to know the land use pattern. In the same way, the various LULC image classified which has collected from Institute of Remote Sensing (IRS), scanned and digitized using Arc GIS software. The agricultural practices under agriculture land and cropland has most important crash over the hydrological processes of the watershed. Thus, the change detection obtained from LULC serve in most favorable solutions for the selection, planning, implementation and monitoring of development schemes to meet the increasing demands of human needs has lead to land management. The Remote Sensing techniques also cost effective to detect the change in LULC over a large area due to natural and human activities. This study shall be very useful for further development planning.
This thesis is based on the application of Internet of Things (IoT) and WebGIS in precision agriculture. Through analyzing the current development of precision agriculture in China and considering its advantages and shortcomings, we choose an ecology farm as an example to conduct a new precision agriculture management system (PAMS) based on the above two techniques. We designed the four architectures of PAMS: the spatial information infrastructure platform, the IoT infrastructure platform, the agriculture management platform and the mobile client. Users can monitor and manage the agriculture production by PAMS. What's more, module integration method and open source software can help us to reduce the development cost and to improve the system efficiency.