Precision Agriculture System Research Articles

Maize2035: A decadal vision for intelligent maize breeding.

Maize, a cornerstone of global food security, has undergone remarkable transformations through breeding, yet it faces mounting challenges in a changing world. In this review, we trace the historical successes of maize breeding which laid the foundation for present opportunities. We examine both the specific and shared breeding goals related to diverse geographies and end-use demands. Achieving these coordinated breeding objectives requires a holistic approach to trait improvement for sustainable agriculture. We discuss cutting-edge solutions, including multi-omics approaches from single-cell analysis to holobionts, smart breeding with advanced technologies and algorithms, and the transformative potential of rational design with synthetic biology. A transition towards a data-driven future is currently underway, with large-scale precision agriculture and autonomous systems poised to revolutionize farming practice. Realizing these futuristic opportunities hinges on collaborative efforts spanning scientific discoveries, technology translations, and socioeconomic considerations in maximizing human and environmental well-being.

Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data

The present study focuses on analysis of the soil cover structure (SCS, SCSs), which is the most detailed level of soil organization in space. The detail in which complex SCS can be studied is often insufficient, since until now it has not been possible to map it over large areas at scales larger than 1:10,000. To increase the detail in which SCS can be studied, the methods of identifying the bare soil surface (BSS) and averaging its multitemporal spectral characteristics were used, which opens up new possibilities for mapping complex SCS over large areas. New SCSs of leached chernozems (Luvic Chernic Phaeozem) were discovered, which can produce patterns on satellite images similar to sections of Timan agate—agate-like soil cover structures (ASCS, ASCSs). ASCSs are formed on Quaternary sediments of varying thickness from 0.3 to 6 m, underlain by carbonate and red sediments of the Permian period. The ASCS pattern is formed by ring-shaped stripes (rings) of different colors and brightness, which are determined by the carbonate and red-colored inclusions involved in the arable horizon. Eight soil varieties were identified to describe ASCSs during the study. According to the WRB, there are six main soil types, and according to the classification of Russian soils in 1977, there are four types. ASCSs were identified over large areas and soil maps of ASCSs were constructed using multitemporal spectral characteristics of the BSS in the form of multitemporal soil line coefficients. Neural networks were used to identify BSS on big remote sensing data. ASCSs have contrasting soil properties and contrasting fertility (productivity of agricultural crops). ASCS maps can serve as the basis for task maps of precision farming systems. Perhaps ASCSs are unique objects for the area of chernozem distribution, where in one soil profile there are rocks with an age from the first thousand years (Quaternary) to 250 million years (Permian). Chernozems are fertile, studied, mercilessly exploited, but sometimes they are simply beautiful—agate-like.

IoT Based Smart Monitoring System for Enhancing Precision Agriculture and Environmental Farming System

IoT Based Smart Monitoring System for Enhancing Precision Agriculture and Environmental Farming System

IoT based intelligent pest management system for precision agriculture

IoT based intelligent pest management system for precision agriculture

Artificial Intelligence in Agriculture: A Technical Analysis of Precision Farming Systems

This technical article comprehensively explores the integration of Artificial Intelligence in agricultural systems, with a primary focus on precision farming methodologies and their practical implementation. It conducts an in-depth examination of the technological framework underpinning agricultural AI systems, encompassing sophisticated data acquisition infrastructure and advanced machine learning implementations. Through detailed analysis, the article investigates the critical technical components of precision farming systems, emphasizing the integration of sensor technologies spanning both aerial and ground-based platforms, alongside robust data analytics architectures. It systematically addresses the prevalent challenges in agricultural data integration and system scalability, presenting innovative solutions for these complex issues. Furthermore, the article explores emerging technological developments in the field, including cutting-edge sensing technologies and AI algorithm enhancements, offering valuable insights into future trajectories of agricultural AI applications. The comprehensive article illuminates how AI technologies are fundamentally transforming traditional farming practices through the implementation of sophisticated automated decision-making processes and enhanced resource management capabilities, ultimately contributing to more efficient and sustainable agricultural operations

Current status, performance characteristics and future perspective of electric tractor

<p align="justify">Electric tractors with its cutting-edge technological improvements represent a transformative shift in agricultural technology for achieving the broad objectives of sustainable and environment friendly farming practices. This review examines the current status, performance characteristics, and future perspectives of electric tractors, focusing on their potential to replace traditional diesel models. The analysis highlights the advantages of electric tractors, including lower operational costs, reduced maintenance, and zero emissions, which contribute to quieter and cleaner agricultural operations. This transformation is witnessing several challenges in terms of high initial costs, limited battery life and the need for extensive charging infrastructure. The performance of electric tractors, particularly in extreme climates, is also a significant concern, with battery efficiency and power output varying under different environmental conditions. Despite these challenges, advancements in battery technology and propulsion systems, along with supportive government policies, are paving the way for greater adoption of electric tractors. The review also discusses the variability in regional adoption rates, with Europe leading due to strong regulatory support, while other regions like North America and Asia-Pacific are witnessing sluggish growth. The integration of electric tractors into precision agriculture and smart farming systems presents exciting opportunities for the future, boosting both productivity and sustainability. The investment in innovations and infrastructure related to electric tractor and renewed supportive policies from the governmental sector can usher a revolution in agricultural production system with lower environmental impact.</p>

Intelligent Field Sensor Station for Monitoring Agrophysical Parameters and Phenotyping in Precision Agriculture System

Current trends in agriculture highlight the widespread adoption of information technology and Internet of Things (IoT) sensor networks for monitoring agrophysical soil parameters and phenotyping objects. This approach enables precise, real-time data analysis, optimizing agricultural processes and supporting the development of adaptive management systems. The integration of information technology with the monitoring of agrophysical parameters and phenotyping objects underscores the strategic importance of this approach, especially in the context of climate variability and the growing need to enhance production sustainability. (Research purpose) To develop an intelligent field sensor station for precision farming that ensures high-accuracy, real-time monitoring of agrophysical parameters and plant phenotyping using an Internet of Things sensor network. (Materials and methods) Existing methods for monitoring agrophysical parameters and phenotyping objects were analyzed. Based on these methods, a design for an intelligent field sensor station was developed, and suitable sensors were selected. (Results and discussion) The intelligent field sensor station successfully demonstrated its efficiency, confirming both its functionality and reliability in simultaneous data collection. The data collected on soil agrophysical parameters, meteorological conditions and plant phenotyping provide extensive knowledge for precision farming and optimizing agricultural processes. (Conclusions) Light gray forest soil with high porosity and neutral pH level provided favorable conditions for crops. Preliminary chemical analysis of the soil revealed moderate levels of organic matter, mobile phosphorus, and potassium, indicating a potentially fertile site. Meteorological data playeda key role in agrometeorological analysis, significantly impacting agricultural processes. The developed station introduces an innovative approach to monitoring agricultural parameters, offering promising prospects for modern agriculture.

Калибровка внутренних эталонов шумового сигнала радиометра по внешним источникам Igor A.

The possibility of using solid-state noise signal sources as internal standards in a two-stop modulation radiometer is discussed. The dependence of the accuracy of radiometric measurements on the temperatures of internal noise standards is estimated. The possibility of calibrating internal solid-state noise sources against external reference objects with known noise temperatures is being investigated. Formulas for calculating the noise temperatures of internal standards based on known temperatures of external standards are given. The prospect of using a new method of calibration of internal standards to improve the accuracy and stability of radiometric sensing of soils for remote determination of moisture content and soil surface temperature as an element of a precision farming system is analyzed.

Nanotechnology for sustainability of agriculture and environment: Green synthesis and application of nanoparticles – A review

Moreover, with the continuous population increase worldwide, there is more demand of food and other daily necessities, which require double agriculture production to provide adequate food for humanity from the same available natural resources. Therefore, looking for new techniques to combat various challenges is instantly needed to sustain crop production and provide enough food for humanity. The use of nano-technology in agriculture field represents an important tool for consistent production of agriculture crops and assists farmers with new practice management systems such as precision agriculture systems, the aim of which is to increase productivity and reduce expenses. Nanotechnology plays an important role in altering agriculture food production. The particles ranging from 1 to 100 nm are accounted as nanoparticles, which usually have high surface energy, big surface area and quantum quarantine. Most notably, nanoparticles increase crop yield by increasing the effectiveness of agricultural inputs to enable site-targeted, controlled nutrient delivery, assuring the least amounts of agro-inputs needed. Additionally, the main aim in agricultural production is to facilitate plants' faster adaptation to the increasingly severe climate change factors, such as high temperatures, low water availability, salinity, alkalinity and environmental pollution with heavy metals, without jeopardizing the delicate ecosystems that are currently in place. This review illustrates the effect of numerous nanoparticles on diverse plants of many concentrations, sizes and shapes. The molecular and genetic response to NPs, their method of action and their interactions with biomolecules should be the main areas of study in the future.

Precision Agriculture System with IoT: An Approach to Increase Production and Efficiency

The application of IoT technology in the agricultural industry has significantly increased efficiency and productivity. In this study, a smart irrigation system that utilizes soil temperature and humidity sensors and NodeMCU ESP8266 and ESP32 units will be explained for its implementation. The NRF24L01 module is a wireless sensor module used to transmit data wirelessly and allows it to cover a wide range. Now, farmers can monitor and control the irrigation system remotely through the Blynk application. Tests show that crops managed through the IoT system have more consistent soil and air conditions and more efficient temperature and humidity control. Therefore, this precision agriculture system utilizes IoT and NRF24L01 to improve water and energy efficiency and encourage sustainability efforts in addressing climate change and increasing global food demand

Main technical requirements for radiometric system for remote soil moisture meter

A dual-polarization L-band microwave radiometer was developed and tested for remote soil moisture measurement. The tests were conducted to study the possible integration of soil moisture and temperature data obtained by the radiometer into the precision farming system. The purpose of this work is to clarify and substantiate the main technical parameters of a new microwave radiometer for determining moisture content portraits of large areas of agricultural land, based on the analysis of the results of flight tests of dual-polarization microwave radiometric remote soil moisture indicator, which were conducted using a quadrocopter in different regions of the Russian Federation. Crop yields are determined by moisture content in the productive layer of soil. To obtain data on soil moisture at different depths it is necessary to use multi-frequency radiometers. So, the use of a multi-frequency microwave radiometer with wavelengths of 21, 11 and 5.5 cm was justified. The mass of such radiometer should not exceed 5 kg. and dimensions not more than 360x360x60 mm. so that it could be used as a carrier of small class UAV with payload not more than 5 kg. The second sensitivity should be no worse than 0.5 degrees. Continuous operation time from the built-in battery is not less than 3 hours.

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.

Preface

The International Conference on Agriculture, Environment and Food Security (AEFS) is a prominent global conference organised by the Faculty of Agriculture at Universitas Sumatera Utara, Indonesia. It provides a platform for sharing innovative and practical insights in agriculture, environment, and food security. Furthermore, it facilitates global communication among scientists, researchers, academics, resource managers, practitioners, students, policymakers, and everyone interested in agriculture, environmental sciences, and food security. The AEFS Conference has been conducted every year since 2017. The AEFS Conference has focused on the following themes over the past four years: Agriculture, Plantation, and Livestock for World Food Security (AEFS 2017); Prospects and Challenges of Sustainable Agriculture to Improve Food Security and Environment (AEFS 2018); Challenges on Food Security and Sovereignty in the Industrial Era 4.0 (AEFS 2019); Innovation and Technologies on Agricultural and Environmental Sustainability Systems (AEFS 2020); Precision Agriculture Technologies and Farming Systems Towards Food Security and Sustainability (AEFS 2021); Strengthening Sustainable Agriculture for A Better Future (AEFS 2022); Green Economy for Enhancing Food Security, Environmental and Agricultural Sustainability (AEFS 2023). In addition, the 8th AEFS 2024 Conference with the theme Eco-Friendly Agricultural to Increase Food System Resilience. List of Honorary Board, Steering Committee, Organizing Committee, Scientific Committee and Editorial and Reviewing Committee are available in this pdf.

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.