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The purpose of this paper is to improve the accuracy and signal-to-noise ratio (SN) of a crop nitrogen sensor.

The accuracy and wide adaptability of two spectral calibration methods for a crop nitrogen sensor based on standard reflectivity gray plates and standard detector, respectively, were compared.

The calibration method based on standard detector could significantly improve the measurement accuracy and the SN of this crop nitrogen sensor. When compared with the method based on standard gray plates, the measurement accuracy and the SN of the crop nitrogen sensor calibrated based on the standard detector method improved by 50 and 10 per cent, respectively.

This research analysed the calibration problems faced by the crop nitrogen sensor (type CGMD302) based on standard gray plates, and proposed a sensor calibration method based on a standard detector. Finally, the results of the two calibration methods were compared in terms of measurement accuracy and the SN of the crop nitrogen sensor.

To describe the function and use of the GreenSeeker™ active remote sensor used to detect crop nitrogen status.

In this paper, the GreenSeeker active remote sensor and its use in irrigated maize production systems will be described. A brief discussion of the science of using remote sensing for studying plants is presented. Additionally, a summary of observations collected from field trials is presented.

The GreenSeeker active sensor has tremendous potential for accurately characterizing crop variability for site‐specific N rate determinations in the Western Great Plains region of the United States.

This paper discusses the GreenSeeker active sensor for detecting crop variability. Data from the GreenSeeker can be used to make site‐specific nitrogen fertilizer applications which may lead to improved nitrogen use efficiency.

The unmanned aerial vehicles (UAVs) entered into their development stage when different applications became real. One of those application areas is agriculture. Agriculture and transport currently follow infrastructure as the top industries in the world UAV market. The agricultural UAV can be acquired as a ready-made, built by its future user or UAV-as-a-service (UaaS) way. This paper aims to help the UAVs’ users to choose the right sensors for agricultural purposes. For that sake, the overview of the types and application areas of onboard sensors is presented and discussed. Some conclusions and suggestions should allow readers to choose the proper onboard sensors set and the right way of acquiring UAVs for their purposes related to the agricultural area.

The agricultural UAVs’ onboard specialised sensors have been analysed, described and evaluated from the farmer’s operational point of view. That analysis took into consideration the agricultural UAVs’ types of missions, sensor characteristics, basics of the data processing software and the whole set of UAV-sensor-software operational features. As the conclusions, the trends in the onboard agricultural UAVs’ sensors, their applications and operational characteristics have been presented.

Services performed by the UAVs for the agriculture businesses are the second in the UAV services world market, and their growth potential is around 17% compound annual growth rate in the next years. As one of the quickest developing businesses, it will attract substantial investments in all related areas. They will be done in the research, development and market deployment stages of that technology development. The authors can expect the new business models of the equipment manufacturers, service providers and sellers of the equipment, consumables and materials. The world agricultural UAVs’ services market will be divided between the following two main streams: the UAVs’ solutions dedicated to the individual farmers, systems devoted to the companies giving the specialised services to individual farmers, in the form of UaaS. It will be followed by the two directions of the agriculture UAV set optimisation, according to each of the above streams’ specific requirements and expectations. Solutions for the individual users will be more straightforward, universal and more comfortable to operate but less effective and less accurate than systems dedicated to the agricultural service provider. UAVs are becoming important universal machines in the agriculture business. They are the newcomers in that business but can change the processes performed traditionally. Such an example is spraying the crops. UAVs spray the rice fields in Japan on at least half of them every year. The other is defoliating the cotton leaves, which only in one China province takes place on a few million hectares every year (Kurkute et al., 2018). That trend will extend the range of applications of UAVs. The agricultural UAV will take over process after process from the traditional machines. The types and number of missions and activities performed by agricultural UAVs are growing. They are strictly connected with the development of hardware and software responsible for those missions’ performance. New onboard sensors are more reliable, have better parameters and their prices are reasonable. Onboard computers and data processing and transmitting methods allow for effective solutions of automatisation and autonomy of the agricultural UAVs’ operation. Automatisation and autonomous performance of the UAVs’ agricultural missions are the main directions of the future development of that technology. Changing the UAV payload allows for its application to a different mission. Changing the payload, like effectors, is quite simple and does not require any special training or tooling. It can be done in the field during the regular operation of the agricultural UAV. Changing the sensor set can be more complicated, because of the eventually required calibrating of those sensors. The same set of sensors gives a possibility to perform a relatively broad range of missions and tasks. The universal setup consists of the multispectral and RGB camera. The agricultural UAV equipped with such a set of sensors can effectively perform most of the crop monitoring missions. The agriculture business will accept the optimised sensor-computer-software UAV payload set, where its exploitation cost and operational simplicity are the critical optimisation factors. Simplicity, reliability and effectiveness of the everyday operation are the vital factors of accepting the agricultural UAV technology as a widespread working horse.

Performed research studies have been done taking into consideration the factors influencing the real operational decisions made by the farmers or companies offering UAV services to them. In that case, e.g. the economical factors have been considered, which could prevail the technical complexity or measuring accuracy of the sensors. Then, drawn conclusions can be not accurate from the scientific research studies point of view, where the financing limits are not so strict.

The main goal of the paper is to present the reasons and factors influencing the “optimised” solution of the configuration of agricultural UAV onboard sensors set. It was done at the level useful for the readers understanding the end-users expectations and having a basic understanding of the sensors-related technologies. The paper should help them to configure an acceptable agricultural UAV for the specific missions or their servicing business.

Understanding the technology implications related to the applying of agricultural UAVs into everyday service is one of the main limits of that technology market deployment. The conclusions should allow for avoiding the misunderstanding of the agricultural UAVs’ capabilities and then increasing their social acceptance. That acceptance by the farmers is the key factor for the effective introduction of that technology into the operation.

Presented conclusions have been drawn on the base of the extensive research of the existing literature and web pages, and also on the own experience in forestry and agriculture and other technical applications of the onboard sensors. The experience in practical aspects of the sensors choosing and application into several areas have been also used, e.g. manned and unmanned aeroplanes and helicopters applied in similar and other types of missions.

There is an urgent need to develop climate-smart agrosystems capable of mitigating climate change and adapting to its effects. Conventional agricultural practices prevail in Mauritius, whereby synthetic chemical fertilizers, pesticides and insecticides are used. It should be noted that Mauritius remains a net-food importing developing country of staple food such as cereals and products, roots and tubers, pulses, oil crops, vegetables, fruits and meat (FAO, 2011). In Mauritius, the agricultural sector faces extreme weather conditions like drought or heavy rainfall. Moreover, to increase the crop yields, farmers tend to use 2.5 times the prescribed amount of fertilizers in their fields. These excess fertilizers are washed away during heavy rainfall and contaminate lakes and river waters. By using smart irrigation and fertilization system, a better management of soil water reserves for improved agricultural production can be implemented. Soil Nitrogen, Phosphorus and Potassium (NPK) content, humidity, pH, conductivity and moisture data can be monitored through the cloud platform. The data will be processed at the level of the cloud and an appropriate mix of NPK and irrigation will be used to optimise the growth of the crops. Machine learning algorithms will be used for the control of the land drainage, fertilization and irrigation systems and real time data will be available through a mobile application for the whole system. This will contribute towards the Sustainable Development Goals (SDGs): 2 (Zero Hunger), 11 (Sustainable cities and communities), 12 (Responsible consumption and production) and 15 (Life on Land). With this project, the yield of crops will be boosted, thus reducing the hunger rate (SDG 2). On top of that, this will encourage farmers to collect the waters and reduce fertilizer consumption thereafter sustaining the quality of the soil on which they are cultivating the crops, thereby increasing their yields (SDG 15).

The Internet of Things (IoT) is becoming increasingly popular in agribusiness to help increase food production capacity for the ever-expanding global population. This chapter provides a holistic overview of the latest trends around the applications of IoT in agriculture. We begin by giving an overview of IoT and its capabilities, followed by a deep dive into the practical and realistic aspects of leveraging IoT into the agroecosystem. IoT is already being used for many intelligent agriculture applications, such as open-field agriculture, controlled environment agriculture (greenhouse), livestock breeding, agricultural machinery, and more. This chapter examines those applications and ventures beyond the farm into several other aspects of the ecosystem, including storage, warehouse ambiance control, agri-data analytics and decision control, logistics, environmental safety, etc. The contents of the chapter would be based on extensive studies and empirical analysis of the latest research papers on this subject from around the globe, accurately interpreted and transformed by the authors in light of their academic background and professional experience in the digital transformation arena.

This study aims to illustrate the growing role that sensors play in agriculture, with an emphasis on precision agricultural practices.

Following a short introduction, this study first provides an overview of agricultural measurements and applications. It then discusses the importance of airborne and land-based optical sensing techniques and the role of the normalised difference vegetation index. Sensors used on conventional and robotic agricultural machines are considered next, and fixed sensors and sensor networks are then discussed. Finally, brief concluding comments are drawn.

This shows that much modern agriculture is a high-technology business which relies on a multitude of sensor-based measurements. Sensors are based on a diversity of optical and other technologies and measure a wide range of physical and chemical variables. They are deployed in the air, on agricultural machines and in the field and generate data that can be used to enhance productivity and reduce both costs and the impact on the environment.

This provides a detailed insight into the important role played by sensors in modern agricultural practices.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

The purpose of this paper is to provide an overview of smart agriculture systems and monitor and identify the technologies which can be used for deriving traditional agriculture system to modern agriculture system. It also provides the reader a broad area to work for the advancement in the field of agriculture and also explains the use of advanced technologies such as spectral imaging, robotics and artificial intelligence (AI) in the field of agriculture.

Smart uses of modern technologies were reviewed in the field of agriculture, which helps to monitor stress levels of plants and perform operations according to requirements. Operations can be irrigation, pests spray, monitoring crops, monitoring yield production, etc. Based on the literature review, a smart agriculture system is suggested. The parameters studied were spectral image processing, AI, unmanned aerial vehicle (UAVs) (fixed and rotatory), water or soil moisture, nutrients and pesticides.

The use of autonomous vehicles and AI techniques has been suggested through which the agriculture system becomes much more efficient. The world will switch to the smart agriculture system in the upcoming era completely. The authors conclude that autonomous vehicle in the field of science is time-saving and health efficient for both plants and workers in the fields. The suggested system increases the productivity of crops and saves the assets as well.

This review paper discusses the various contemporary technologies used in the field of agriculture and it will help future researchers to build on this research. This paper reveals that the UAVs along with multispectral, hyperspectral or red, green and blue camera (depends on the need) and AI are more suitable for the advancement of agriculture and increasing yield rate.

The elevated level of nitrate in groundwater is a serious problem in Texas aquifers. To control and manage groundwater quality, the characterization of groundwater contamination and identification of the factors affecting the nitrate concentration of groundwater are significant. The purpose of this paper is to determine factors which have significant impacts on the elevated groundwater nitrate concentrations of the Southern High-Plains and the Edwards-Trinity aquifers.

The characterization of groundwater nitrate contamination was undertaken by analyzing the hydrochemical data of groundwater within a statistical framework. The multivariate statistical analysis (ordinary least square) and geographically weighted regression (GWR) models were used to study the relationship between groundwater nitrate contamination and land use of the study areas.

Results show groundwater nitrate contamination is typically due to an overapplication of N fertilizers to cotton in the Southern High-Plains aquifer and to grassland in the Edwards-Trinity aquifer. Adjusted R² (0.45) explains variations of nitrate concentration by well-depth, cotton production, shrubland and grassland in the Edwards-Trinity aquifer. The results of an analysis of variations in N concentration with well depth for all 192 wells indicate that nitrate concentrations in water from wells in the Southern High-Plains and Edwards-Trinity aquifers tend to decrease with increasing well-depth.

In this study, the GWR model was built to identify nitrate concentration within a geographic framework to ensure sustainable use of groundwater, which is important for local management purposes. The analysis should include local spatial variations of elements such as hydrologic characteristics and the land use activities if groundwater nitrate contamination causes adverse effects on human and ecosystem health.

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.