Precision Agriculture System Research Articles

Automated control complex of agrotechnology in precision farming

The paper presents a project of an automated complex for managing agricultural technology. The complex consists of two inseparable components, a control unit and an executive robotic technological machine. The control unit implements the modern theory of controlling a complex dynamic system, which is an agricultural field with sowing crops. The main distinctive feature of the proposed management theory is that the object of management is an agrocenoses, which includes sowing crops and weeds. This feature is transferred to the executive technological machine, through which the simultaneous application of mineral fertilizers and herbicide treatment is carried out. At the same time, the formation of optimal technological operations is carried out on the basis of Earth remote sensing data. Based on these data, the parameters of the state of the agrocenoses are assessed, and the resulting estimates are system-wide feedback through which agricultural technology is managed. The presented complex is of interest to specialists developing individual components of modern precision farming systems.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

Strategic Management of Eco-Innovation and Emerging Technologies for Sustainability in Agro-Based Industries

Eco-innovation and emerging technologies are increasingly central to driving sustainability in agro-based industries. This study examines the impact of technological advancements on sustainable agricultural practices, focusing on innovations that reduce environmental impacts while enhancing productivity. According to the USDA, precision agriculture systems implemented in the Midwest show a 15-20% reduction in fertilizer use and a 10-15% increase in crop yields. Furthermore, the adoption of renewable energy solutions, such as solar-powered irrigation in India, has led to a 30% decrease in energy costs and a 25% reduction in greenhouse gas emissions (GHGs), as reported by the International Renewable Energy Agency (IRENA). Biotechnology applications, such as genetically modified drought-resistant crops, have reduced pesticide use by up to 37% while increasing crop resilience, according to a study published in Nature. This paper also explores the socio-environmental implications of these technologies, emphasizing the need for balanced development that integrates technological progress with ecological sustainability. Case studies from Brazil, India, and Kenya provide further insights into the successful implementation of these innovations. The findings suggest that leveraging emerging technologies in agro-based industries can significantly enhance sustainability, contributing to a more resilient and equitable human-environment relationship.

Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks

Precision agriculture has emerged as a promising approach to enhance crop yield, reduce environmental impact, and optimize resource utilization through advanced sensing and automation technologies. This paper proposes an energy-efficient and location-aware framework for Internet of Things (IoT) and Wireless Sensor Networks (WSN)-based precision agriculture systems. The framework leverages low-power wireless communication protocols, adaptive sensor scheduling, and location-based clustering algorithms to minimize energy consumption and prolong the network lifetime. Key features include real-time monitoring of soil moisture, temperature, humidity, and crop health through geographically distributed sensors, with automated decision-making for irrigation, fertilization, and pest control. The proposed framework also integrates machine learning models for predictive analysis and anomaly detection, enabling early identification of potential issues that could adversely affect crop productivity. Simulation results demonstrate a significant reduction in energy consumption and communication overhead, while maintaining high accuracy in environmental parameter monitoring and resource allocation. This framework offers a scalable and robust solution for implementing sustainable precision agriculture practices, particularly in remote and resource-constrained areas

Efficiency of precision farming systems application in the implementation of main tillage

Abstract The main direction of crop production development is transformation from traditional agro-technologies to resource- and energy-saving technologies. One of the options of developing resource- and energy-saving technologies is precision farming technologies. Implementation of these technologies will provide optimal results in crop production. However, the use of tractors, agricultural machines and combines in the system of effective precision farming is possible, based on the analysis and substantiated conclusions of modern scientific and technical information. As well as the possibilities for specific natural and production conditions. Thus, the purpose of the study is to increase the efficiency of precision farming systems in the implementation of main tillage. The article presents the results of comparative tests of disc ripper “John Deere” (model 2720) in combination with tractors “Claas” (model AXION 950). The tests were conducted in the process of main tillage in the system of precision agriculture. The fields of “Agricultural Experimental Station “Zarechnoye” LLP, located in Kostanay region, Kostanay oblast, were chosen as a testing ground. During the comparative testing process, the objective was to determine the impact of the automatic driving system “Claas GPS PILOT” on certain indicators, in particular agrotechnical, technical-operational and economic indicators of their functioning. The conditions of comparative tests are typical for farms located in the areas of Northern Kazakhstan producing grain crops. The results of tests demonstrated that the application of automatic driving system “Claas GPS PILOT” on main tillage, disc ripper “John Deere” (model 2720) in combination with a tractor “Claas” (model AXION 950) allows to increase the working width of the machine by 24 cm, increase the shift productivity of the machine by 4.8%, reduce specific fuel consumption by 4.9%, specific energy costs by 2.9-6.1%, total cash outlay by 521.0 tenge/ hectare and get annual savings of total cash outlay of 294.5 thousand tenge.

How artificial intelligence plays a role in achieving sustainable development goals?

Artificial Intelligence (AI) is increasingly recognized as a key enabler in achieving Sustainable Development Goals (SDGs) due to its transformative potential across various sectors. This paper explores the intersection of AI and SDGs, highlighting the significant role AI plays in accelerating progress towards sustainability. AI technologies such as machine learning, natural language processing, and computer vision have been instrumental in enhancing efficiency, decision-making, and resource management in areas such as healthcare, agriculture, education, and climate change mitigation. By analyzing vast amounts of data, AI can provide valuable insights and predictive models to inform policy-making, optimize resource allocation, and enhance monitoring and evaluation processes. Furthermore, AI empowers developing countries to leapfrog traditional developmental barriers by offering innovative solutions that are cost-effective and scalable. Through initiatives like precision agriculture, telemedicine, and smart energy systems, AI enables inclusive growth while reducing inequalities and enhancing resilience to environmental challenges. While AI brings immense opportunities for sustainable development, challenges such as data privacy, bias, and ethical concerns must be addressed to ensure that AI technologies are deployed responsibly and equitably. Collaborative efforts between governments, industry, and civil society are essential to harness the full potential of AI in achieving the SDGs and creating a more inclusive and sustainable future for all.

Role of Entomology in the Era of Precision Agriculture: A Review

Precision agriculture (PA) represents a transformative approach to farming by utilizing advanced technologies like GPS remote sensing, and data analytics to optimize crop production and minimize environmental impacts. While technological advancements in PA are well-documented, the role of entomology is often overlooked. This review highlights the critical intersection between entomology and PA, demonstrating how understanding insect behavior, population dynamics, and ecological interactions enhances PA practices. Insects play pivotal roles in pest management, pollination, and ecosystem health, making their study essential for developing effective PA strategies. The review examines how entomological research has informed precision pest management, integrated pest management (IPM) systems, and the use of biological control agents within the PA framework. It discusses how integrating entomological insights with PA technologies, such as remote sensing and sensor networks, enables real-time monitoring and targeted pest control, thus improving crop yields and reducing pesticide use. Additionally, the review addresses challenges such as data integration, technology accessibility, resistance management, and environmental concerns, and highlights prospects including advancements in technology, innovation in monitoring techniques, and the integration of biological control methods. By emphasizing the need for interdisciplinary collaboration, this review underscores the importance of combining entomological knowledge with PA tools to develop more sustainable and efficient agricultural practices. The integration of entomology into PA not only enhances pest management but also contributes to broader goals of environmental sustainability and agricultural resilience. Overall, this review offers a comprehensive overview of how entomology can advance precision agriculture and foster sustainable food production systems.

A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture.

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Analysis of test results of the P-20 multi-rotor UAV when spraying pesticides in precision agriculture

Abstract The article provides an overview of the current results of using multi-rotor UAVs when spraying pesticides in precision agriculture. It is noted that, as part of improving the technology for using UAVs, it is necessary to continue work on optimizing and testing the parameters of UAV transport and technological cycles together with a reasonable choice of special reagents and their combinations to improve the spraying effect and increase the level of use of pesticides. This will directly affect the improvement of the quality and efficiency of precision farming systems. The analysis presented in the work is based on testing the parameters of the transport and technological cycle of a multi-rotor UAV of the P-20 type, which was used to spray pesticides on cotton fields to combat aphids and spider mites on cotton at the flowering and boll setting stages. The results are considered that make it possible to analyze and compare the laws of uniform and penetrating deposition and drift of droplets in cotton canopies at different UAV flight altitudes. It has been shown that spraying small volumes of agrochemicals using UAVs at low altitudes differs from operations performed using modern manned aircraft or boom sprayers. The flight altitude of the UAV has a significant impact on the deposition and drift of droplets. The results of studies related to elucidating the effect of defoliant adsorption on cotton leaves are also noted. The adsorption of acetamiprid and spirodiclofen was studied. Methods for statistical processing of the obtained experimental data are presented, in particular, to test the significance of the influence of flight altitude on the experimental results.

Revolutionizing Farm Management with Modern Agricultural Extension Techniques: A Review

The transformative impact of modern agricultural extension techniques on farm management in India, highlighting the integration of digital technologies, precision agriculture, and innovative management systems to enhance productivity and sustainability. Traditional farming practices in India, characterized by low efficiency and productivity, are undergoing significant changes with the introduction of technologies such as mobile apps, remote sensing, GIS, and sensor-based systems. These technologies facilitate improved crop monitoring, resource mapping, and data-driven decision-making, leading to enhanced crop yields, resource efficiency, and economic benefits. Notably, precision agriculture technologies like automated systems and IoT have enabled real-time, precision farming practices that significantly reduce resource waste and increase crop productivity. However, the adoption of these modern techniques is not without challenges. Issues such as accessibility, the digital divide, economic constraints, and the need for continuous training hinder widespread implementation. The review also discusses policy recommendations for supporting the adoption of these technologies, including increased government investment in infrastructure, regulatory frameworks, and capacity building. The integration of these modern techniques with global agricultural trends-especially those aimed at sustainability and climate change adaptation-is crucial for the future of Indian agriculture. By leveraging advanced technologies and aligning with global standards, India can not only enhance its agricultural productivity but also improve the socio-economic status of its rural population, contributing to broader economic and environmental goals.

Hazelnut mapping detection system using optical and radar remote sensing: Benchmarking machine learning algorithms

Mapping hazelnut orchards can facilitate land planning and utilization policies, supporting the development of cooperative precision farming systems. The present work faces the detection of hazelnut crops using optical and radar remote sensing data. A comparative study of Machine Learning techniques is presented. The system proposed utilizes multi-temporal data from the Sentinel-1 and Sentinel-2 datasets extracted over several years and processed with cloud tools. We provide a dataset of 62,982 labeled samples, with 16,561 samples belonging to the ‘hazelnut’ class and 46,421 samples belonging to the ‘other’ class, collected in 8 heterogeneous geographical areas of the Viterbo province. Two different comparative tests are conducted: firstly, we use a Nested 5-Fold Cross-Validation methodology to train, optimize, and compare different Machine Learning algorithms on a single area. In a second experiment, the algorithms were trained on one area and tested on the remaining seven geographical areas. The developed study demonstrates how AI analysis applied to Sentinel-1 and Sentinel-2 data is a valid technology for hazelnut mapping. From the results, it emerges that Random Forest is the classifier with the highest generalizability, achieving the best performance in the second test with an accuracy of 96% and an F1 score of 91% for the ‘hazelnut’ class.

The Choice of a Promising Design of a Fertilizer Mixing Unit Using Computer Simulation Technologies

The article deals with applying solid mineral fertilizers within the framework of the precision farming system. The use of complex mineral fertilizer compositions in the fields with an uneven demand for macroelements leads to the bigger amount of fertilizer applied, which has a detrimental effect on the ecology, the physical condition of the soil and the crop itself. It is proposed to use fertilizer mixtures the composition of which has been adjusted in fertilizer mixing units. The existing mixers have been analyzed, the processes occurring during operation of the screw, drum and centrifugal mixers were simulated. The results of the simulation showed that the unevenness coefficient kс for the drum-type mixer was 8.6%, for the blade-type mixer 17.3, and for the centrifugal mixer 3.5%.

UAV-Assisted VLC Using LED-Based Grow Lights in Precision Agriculture Systems

UAV-Assisted VLC Using LED-Based Grow Lights in Precision Agriculture Systems

IOT Based Automated Hydroponics System for Precision Agriculture

Growing plants hydroponically can be a great option for plants that are traditionally hard to grow in soil due to specific requirements. The aim of this work is to design and construct an indoor automatic vertical hydroponic system that does not depend on the outside climate. The designed system is capable to grow common type of crops that can be used as a food source inside homes without the need of large space. The design of the system was made after studying different types of vertical hydroponic systems in terms of price, power consumption and suitability to be built as an indoor automated system. A microcontroller was working as a brain of the system, which communicates with different types of sensors to control all the system parameters and to minimize the human intervention. An open internet of things (IoT) platform was used to store and display the system parameters and graphical interface for remote access. The designed system is capable of maintaining healthy growing parameters for the plants with minimal input from the user. The functionality of the overall system was confirmed by evaluating the response from individual system components and monitoring them in the IoT platform

Design and Development of an Intuitive Android Application for Smart Farming

Farmers face challenges in adapting to environmental changes, yet with decreasing implementation costs and a growing accessibility to technical expertise, progress is evident. This research introduces KisanVishwa, an Android app designed for smart farming. KisanVishwa optimizes precision agriculture systems, enabling farmers to monitor their fields in real time and address agricultural constraints. Tailored to meet the specific needs of today's farmers, the app provides user-friendly tools and the latest information on farm equipment, fertilizers, chemicals, technological advancements, and local distributors and retailers. By integrating KisanVishwa into their operations, farmers can overcome challenges associated with the adoption and maintenance of precision agriculture. KisanVishwa adopts a user-centric design for compatibility across diverse Android devices, integrating monitoring tools and collaborating with agricultural research institutions. Empowering farmers, the app addresses contemporary challenges, fostering informed decisions and efficiency. With its user-friendly interface, KisanVishwa promotes the adoption of precision agriculture, essential resources, and sustainable practices in the agricultural sector. Convincing traditional farmers to adopt advanced technologies may pose challenges. Functionality may depend on reliable internet, posing challenges in areas with limited connectivity.

Transforming Agriculture through Artificial Intelligence: Advancements in Plant Disease Detection, Applications, and Challenges

Artificial intelligence (AI) has emerged as a revolutionary tool in agriculture, particularly in the realm of plant disease detection. This article provides an overview of AI-powered plant disease detection methods, their applications, and the limitations associated with their implementation. By leveraging AI, farmers can enhance crop management practices, optimize resource utilization, and mitigate yield losses caused by plant diseases. However, challenges such as data scarcity, model interpretability, and deployment in resource-constrained environments remain significant barriers to widespread adoption. Addressing these limitations is crucial for maximizing the potential of AI in revolutionizing agriculture and ensuring global food security. Artificial intelligence (AI) has emerged as a pivotal tool in modernizing agriculture, particularly in the domain of plant disease detection. This article presents a comprehensive examination of AI-driven methodologies for plant disease detection, exploring their applications and inherent limitations. Through the utilization of machine learning and computer vision techniques, AI facilitates early disease identification, precision agriculture, disease surveillance, and decision support systems. Despite these transformative capabilities, challenges such as inadequate data availability, model interpretability, and implementation in resource-constrained settings impede widespread adoption. Addressing these obstacles is imperative for fully harnessing the potential of AI in agricultural innovation, thereby safeguarding global food security and sustainability.

Waste Utilization in Horticulture: An Overview

Waste management in horticulture plays a pivotal role in achieving environmental sustainability and optimizing resource utilization. One of the primary methods for waste utilization is composting, which involves the decomposition of organic waste materials to produce nutrient-rich compost. Compost serves as an excellent soil amendment, enhancing soil fertility and promoting plant growth. Additionally, anaerobic digestion offers a valuable approach for converting organic waste into biogas, a renewable energy source that can be used for heating, electricity generation, and vehicle fuel. Furthermore, fermentation processes can be employed to convert fruit and vegetable waste into valuable products such as animal feed and biofuels. The by-products of fermentation, including yeast and enzymes, have applications in various industries, contributing to the circular economy model. Advancements in precision agriculture and smart waste management systems have revolutionized waste utilization in horticulture. Technologies such as GPS, GIS mapping, wireless sensor networks and big data analytics enable data-driven, decision-making and optimize resource allocation. Precision planting, targeted spraying, automated pruning/harvesting and variable rate irrigation are among the innovative practices facilitated by these technologies, leading to increased productivity and efficiency in horticultural operations. Moreover, waste utilization in horticulture contributes to mitigating environmental pollution and reducing greenhouse gas emissions. By diverting organic waste from landfills and incineration, the emission of methane, a potent greenhouse gas, is minimized. Instead, organic waste is repurposed into valuable resources, thereby closing the loop on waste streams and promoting a circular economy.

Web-based spatial decision support system for precision agriculture: A tool for delineating dynamic management unit zones (MUZs)

Precision agriculture (PA) is a farming management concept that aims to provide agronomic, economic, and environmental benefits. One of the fields of research in PA is the delineation of Management Unit Zones (MUZs). MUZs are the sub-division of fields featuring an inter-zonal variation delineated by agronomists for on-field PA operations. To develop MUZs, three factors typically need to be considered: input multi-dimensional data, procedures to process the information, and the optimal number of zones a field should be divided into. PA uses digital technologies to collect and analyze a large amount of data, outline MUZs, monitor crops, and carry out site-specific crop management. Web-based spatial decision support systems (WB-SDSS) can provide users with tools that ease the complex procedures for PA. The objective of this study is twofold: on the one hand, we developed a free and open source (FOSS) WB-SDSS to facilitate the implementation and use of such tools for delineating MUZs and monitoring crops; on the other, a MUZs outline procedure was developed based on Sentinel-2 and Planetscope time series data, and spatio-temporal dynamic clustering model using fuzzy c-means. Our study highlighted that the WB-SDSS might be a helpful solution for harmonizing data collected from different sources, easing the implementation and use of complex geospatial procedures for PA, and delineating MUZs. We tested the system on a particularly representative farm in the Emilia Romagna region (Northern Italy), with 512 hectares of durum wheat crops. Using the WB-SDSS, we quickly delineated homogeneous zones for 27 fields in the study area during the phenological cycle of durum wheat (November 2018-June 2019).

Modeling and investigation of the hydrological situation

Thousands of earthen dams have been built in Russia to create accumulating reservoirs, protect low-lying areas from floods, hydropower and others. Sometimes, due to the presence of underground leaks, erosion of the dam occurs and a breakthrough with catastrophic consequences. Therefore, the task of timely identification of places of underground leaks of earthen dams is urgent. Objective – to show the results of a flight experiment and the practical use of a microwave radiometer mounted on an unmanned aerial vehicle (UAV) in order to monitor the condition of an earthen dam in the Voronezh region and the possibility of searching for places of underground leaks. The paper presents the results of an experimental study of the condition of earthen dams. Also, the analysis of the obtained soil moisture maps allows you to determine the locations of possible underground leaks. The results of a flight experiment using a bipolarization microwave radiometer placed on board an unmanned aerial vehicle for remote determination of a portrait of soil moisture can be used to search for places of possible underground leaks. Also, this method of remote measurement of soil moisture can be used for the needs of agriculture, in particular for precision farming systems. In addition, the paper assesses the prospects of using microwave radiometric moisture meters for operational examination of the state of extended hydrological objects.

DEVELOPMENT OF THE SOWING SYSTEM OF THE SOWING COMPLEX FORINTERNAL-SOIL DIFFERENTIATED MINERAL FERTILIZER WITH SIMULTANEOUS SOWING OF CEREAL

The issue of increasing the effectiveness of post-sowing intra-soil differentiated application of the main and starting dose of granular mineral fertilizers in the system of precision agriculture is considered. The need to use differential application of granular mineral fertilizer and machines that ensure high-quality performance of this technological process, taking into account the heterogeneity of the content of nutrients in the soil in certain areas of the field, is indicated. It is also indicated the need to develop and introduce into production highly accurate automated information technologies based on the use of high-performance means of mechanization. The indicated shortcomings of modern seeding machines: lack of adaptability for simultaneous differentiated introduction of the main and starting dose, as a result of which they lack highly adapted dispensers, appropriate control and management systems. The construction and technological scheme of the seeding complex is proposed and the pneumatic seeding system of this unit is developed for post-sowing intra-soil differentiated application of the main and starting dose of granular mineral fertilizers in the precision farming system. The proposed seeding complex will make it possible to eliminate the excessive application of mineral fertilizers, which can cause an increase in the activation of microbiological processes in the soil and have negative ecological consequences, leading to the deterioration of the physic-chemical and biological properties of the soil. The use of resource-saving technologies of precision agriculture in the agro-industrial complex of Ukraine will allow to ensure the management of production processes at the expense of information technologies, robotic and automated systems, which will allow to bring domestic agricultural enterprises to a new level.