AI-Driven Precision Agriculture: Optimizing Crop Yield and Resource Efficiency

Using data from a survey of cotton producers in 14 US states, and a bivariate probit regression, this study examined the effects of the following measured parameters on the adoption of Automatic Section Control (ASC) technologies and GPS Auto-Guidance (AG) systems: age, education, farm size, field geometry, information sources, as well as the use of specific production practices and other Precision Agriculture (PA) technologies. Results suggest that younger, more educated producers, consulting farm dealers for information about PA technologies, using other PA technologies, and managing larger farming operations located in counties with more irregularly shaped fields are more likely to adopt ASC technologies and AG systems. Predicted adoption probabilities estimated using regression results suggest the use of other PA technologies and farm dealers as a source of precision farming information have the largest impact on the probability of adopting ASC by cotton farmers. Additionally, these results suggest farmers with operations in eastern Arkansas, western Tennessee, and a couple of counties in middle Tennessee are more likely to adopt ASC technologies. Producers in these regions had the highest percentages of users of other PA technologies and farm dealers to obtain PA information.

Modeling soil organic carbon and carbon dioxide emissions in different tillage systems supported by precision agriculture technologies under current climatic conditions

This presentation delves into the evolution and real-world use of a crop recommendation system powered by machine learning. By harnessing historical agricultural data and key environmental variables, the system offers farmers personalized crop choices that promote higher productivity. Drawing on diverse data sources and cutting-edge algorithms, these AI-based solutions have shown impressive gains in performance—boosting efficiency, enhancing precision, and supporting more sustainable practices. Among the notable advancements are a 15% increase in the accuracy of yield predictions, a reduction in water usage by up to 30%, and a 20% decrease in fertilizer application, all without sacrificing output. While the journey toward implementation isn’t without its hurdles—such as concerns around data privacy and the need for upfront investments—the long-term advantages of adopting AI in agriculture are compelling. These include better profitability for farmers, improved ecological balance, and stronger food security. Ultimately, this in-depth analysis sheds light on the transformative power of AI-driven precision agriculture. It reveals how these technologies are redefining traditional farming methods and paving the way to solving global food production challenges, all while lessening the environmental impact of agriculture. Key Words: Various sources include historical crop data, weather records, soil analysis reports, and satellite imagery.

Agricultural sector is undergoing a significant transformation to address the growing global demand for food while ensuring sustainability and minimizing environmental impact. Traditional farming methods, which rely heavily on manual processes and offer limited data insights, are increasingly inadequate for managing the complexities of modern agricultural needs. To overcome these challenges, the integration of advanced computing technologies specifically cloud, edge, and fog computing has emerged as a transformative solution in smart farming. This paper provides a comprehensive literature review on the role of these technologies in agriculture, focusing on their applications, benefits, challenges, and future directions. A proposed framework for integrating IoT, edge, fog, and cloud computing is presented, aiming to enhance productivity, resource efficiency, and environmental sustainability. The framework is evaluated through a case study conducted in the Maregaon District of Yavatmal (Maharashtra), demonstrating significant improvements in crop yields, resource utilization, and decision-making capabilities. The results highlight an accuracy of 95% in data collection and processing, a 20% increase in crop yields, and a 30% reduction in water usage. These findings underscore the potential of these technologies to revolutionize agriculture, ensuring food security and environmental sustainability in the face of growing global challenges.

‘Precision Farming’ or ‘Precision Agriculture’ aims at increasing productivity, decreasing production costs and minimizing the environmental impact of farming. In this context, the present study has been undertaken to understand the impact of precision farming on resource-poor regions and underprivileged farmers. Specifically, the study has looked into productivity, income, employment, and adoption behaviour of technology in agriculture. The study, conducted in the Dharmapuri district, has collected data on precision and non-precision farmings through the interview schedule during the year 2007. Sources of the productivity difference between the precision and conventional farmings have been identified by decomposing the productivity change. Financial impact of adoption has been studied through a two-stage econometric model. The first stage of the model consists of an adoption decision model that describes the factors which influence the likelihood of adopting precision farming. Results of first stage have provided input for the second stage of the model, which has been used to estimate the impact of precision farming on farm financial performance. The study has revealed that adoption of precision farming has led to 80 per cent increase in yield in tomato and 34 per cent in brinjal production. Increase in gross margin has been found as 165 and 67 per cent, respectively in tomato and brinjal farming. The contribution of technology for higher yield in precision farming has been 33.71 per cent and 20.48 per cent, respectively in tomato and brinjal production. The elasticity of 0.39 for the adoption in tomato and 0.28 in brinjal has indicated that as the probability of adoption increases by 10 per cent, net return increases by 39 per cent and 28 per cent in tomato and brinjal cultivation. Lack of finance and credit facilities have been identified as the major constrains in non-adoption of precision farming. The study has suggested that providing of subsidies for water-soluble fertilizers and pump-sets will increase adoption of precision farming.

Mobile internet is considered one of the most important developments in information and communication technology due to its considerable effect on both the economy and our daily lives. Furthermore, mobile internet is an essential tool for overcoming the rural–urban digital divide. With respect to agriculture, mobile internet can play a central role in information gathering as well as the implementation of precision and smart farming technologies. Yet, no study has identified the determinants of mobile internet adoption in agriculture. Using a bivariate probit model with a sample selection and a representative data set from 815 German farmers, this study showed that, among other characteristics, the age of the farmer, farm size and location, as well as familiarity with internet risks is associated with mobile internet adoption in agriculture. These results may be of interest to policy makers, who deal with internet infrastructure, and providers of farm equipment that rely on mobile internet connection. [EconLit citations: Q16].

Smart agriculture using IoT for automated irrigation, water and energy efficiency

The purpose of this research is to study the experience of introducing precision farming technologies abroad and to substantiate the directions of development and practical implementation of these technologies in Belarus. The article discuss-es the leading countries in the field of practical testing of modern technologies of precision farming, analyzes the experience of their use in the EAEU countries, studies the developments in the main directions of the introduction of precision farming in Belarus: the use of GPS for determining field boundaries, remote sensing of the earth, mapping and monitoring the state of crops and productivity; technologies of differentiated application of seeds, fertilizers and pesticides, differentiated pre-sowing soil treatment; technology of parallel driving and automatic control over the work and movement of technical equipment. Based on the results of the analysis, such directions of development and practical use of precision farming tech-nologies in Belarus have been identified, such as the implementation of domestic industrial developments; development of domestic software and hardware; expanding scientific research on precision farming technologies; generalization and dis-semination of scientific and production experience in the introduction of precision farming; development of government measures to support commodity producers, including farms, mastering precision farming technologies. The scientific novelty lies in the study of the problems of introducing precision farming technologies in Belarus, tak-ing into account world experience, as well as in developing priority areas for the further development of precision farming in Belarus. The practical significance lies in the need to introduce precision farming technologies in agricultural organizations of Belarus. It is advisable to continue the research in the direction of developing a methodology for assessing the degree of intro-duction of precision farming technologies, as well as a comprehensive analysis of the results of the practical use of these technologies at micro- and mesoscale levels. Keywords: agriculture, efficiency, sustainability, innovative technologies, precision farming, world experience, de-velopment directions in the Republic of Belarus.

The rapid advancements in Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) are transforming the landscape of agriculture, creating opportunities for smarter, more sustainable farming practices. This book, AI, ML, and IoT for Smart Agriculture: Innovations in Crop Yield, Resource Optimization, and Sustainable Farming Practices, explores the integration of cutting-edge technologies to address the challenges faced by modern agriculture. With the global population continuously rising, the demand for increased agricultural productivity, resource efficiency, and sustainability has never been more pressing. AI and ML offer powerful tools for analyzing vast amounts of agricultural data, driving precision farming practices that can optimize crop yield, improve pest management, and predict climate impacts. IoT-enabled devices provide real-time monitoring and automation capabilities, enhancing resource management and reducing wastage. Through comprehensive research, case studies, and expert insights, this book highlights the transformative potential of these technologies in shaping the future of agriculture. It offers practical guidance for stakeholders, from farmers to policymakers, looking to harness the power of AI, ML, and IoT in achieving sustainable, profitable, and environmentally responsible farming. This resource aims to inspire innovation and promote a holistic approach to the challenges and opportunities of modern agriculture.

Advancements in precision agriculture technologies enable producers to achieve higher yields; however, in some cases, these innovations have not reached widespread adoption despite years of availability. We sought to understand producers’ adoption experiences with two precision agriculture technologies: Nitrogen (N)-Rich Strips and the Sensor Based Nitrogen Rate Calculator (SBNRC). These technologies can help producers optimize their application of nitrogen fertilizer on growing crops, especially small grains such as wheat. Using Rogers’ (2003) diffusion of innovations theory as an explanatory framework, this descriptive-exploratory study examined the adoption behaviors of producers from two midwestern states. Rogers’ (2003) theoretical lens guided instrument development and interpretation of results. To better understand the effects of change agents’ actions and potential adopters’ behaviors during the innovation-decision process, more research is needed regarding disenchantment discontinuance and replacement discontinuance, the potential for pro-innovation bias, and of the innovation attribute compatibility. The future development of precision agriculture technology with the perceptions of potential adopters in mind, especially those averse to adoption and continuance, may assist in overcoming barriers to widespread diffusion.

Purpose Effectively transferring knowledge of digital innovations, such as precision agriculture, is critical for farmers to make adoption decisions to meet current and future farming challenges. Stronger knowledge flows from Extension systems to stakeholders benefit adoption of precision agriculture. Methods This study used a three-round Delphi technique with a panel consisting of 15 precision agriculture experts who are academic extension faculty at land-grant universities in the Southeastern United States to identify the key knowledge, skills, and abilities needed by Extension agents to facilitate effective precision agriculture interactions. Results A total of 13 key competencies were identified. The findings revealed that the majority category identified in the 13 key competencies was the skill component, followed by the knowledge component, indicating the need to expand Extension agents' skills and knowledge related to precision agriculture. Conclusion The top key competency of basic precision agriculture awareness underscores the importance of ensuring Extension agents are aware of precision agriculture applications in their region. While all items were considered relatively important to the panelists based on the rate data, sustainability awareness, and equipment operation skill competencies were ranked as a lower priority, indicating Extension agents do not necessarily need to understand how to use the precision agriculture technology or manage the data but should function as change agents who introduce farmers to precision agriculture and connect them with relevant experts. The findings inform future tailored professional development programs and educational resources for equipping Extension agents with the needed competencies and guide future assessment of precision agriculture competencies among Extension agents.

The Internet of Things (IoT) is transforming agriculture by delivering real-time data and control systems, which optimize resource utilization and efficiency.Conventional irrigation techniques frequently struggle with inefficiencies in maximizing crop yield and water use efficiency (WUE), resulting in reduced crop growth and unnecessary resource waste.However, IoT-based smart irrigation systems make data-driven decisions, which can optimize crop yield and WUE; therefore, improving crop output and water conservation in agriculture.This study focuses on integrating IoT-based smart irrigation against traditional techniques that consider red amaranth cultivation.We developed the irrigation system using a microcontroller (ESP32) and various sensors (soil moisture, humidity, and temperature sensors).The impact of irrigation techniques on red amaranth growth and overall crop output was assessed using key characteristics like plant height, leaf count, area, fresh and dry weight, and water use efficiency.Results show that the IoT-based smart irrigation boosts crop yield by approximately 12% while decreasing water usage by around 58%.

Precision agriculture technologies (PATs) are reshaping modern crop production by promoting efficient and sustainable farming practices. Core components of precision agriculture include remote sensing, GPS-enabled machinery, variable rate technology (VRT), and Internet of Things (IoT)-based systems. Remote sensing tools and unmanned aerial vehicles provide high-resolution data that support accurate assessment of crop health, soil variability, and pest incidence. GPS-guided equipment improves the precision of field operations such as sowing, fertilizer application, and harvesting, leading to reduced input losses and higher operational efficiency. VRT allows site-specific application of water, fertilizers, and pesticides according to crop demand and real-time field conditions, thereby minimizing excessive input use, nutrient runoff, and greenhouse gas emissions. The adoption of precision agriculture significantly enhances environmental sustainability by conserving water, lowering chemical inputs, and improving soil health. By reducing the ecological footprint of farming while increasing yields and profitability, PATs offer a viable solution for meeting the growing global food demand and ensuring long-term agricultural and environmental sustainability. Precision agriculture integrates advanced technologies to enhance farm productivity, efficiency, and sustainability. Precision farming, defined by the strategic application of data analytics and advanced technologies, has emerged as a transformative approach for achieving sustainable agriculture. precision agriculture provides an effective pathway for building sustainable and resilient agricultural systems by improving resource-use efficiency, while simultaneously supporting food security and environmental sustainability.

Adoption of Precision Agriculture (PA) technologies has apparent benefits for agricultural production management, cost savings, and environmental sustainability. However, these technologies are also extremely complicated and influenced by various social and economic forces, which impact their adoption rate, particularly in developing nations. Demographic and agricultural characteristics also play an important role in determining willingness to adopt PA. This study is a survey aimed at establishing the willingness indices of farmers and other relevant personnel to embrace precision farming technologies in Gombe and Bauchi states of Nigeria. The survey included individuals of varied ages; but an average age of 28.5 years for Gombe and 36.5 years for Bauchi, educational levels, and employment statuses, as well as agricultural and operational features. The willingness to adopt PA is high in both states, with 92% in Gombe and 96% in Bauchi. The findings suggest that, while high costs and a lack of technical know-how had a negative impact on readiness to adopt PA in both states, the level of complexity was rated higher by the older generation in Bauchi state, which had a comparatively high mean farmer age. The size of the farm and the number of employees had no direct influence on the adoption of PA technology. Other criteria, such as age and number of years of operation, indicate overlap between the various stages of internet and digital technology adoption, mechanization, and PA. The high willingness rating indicates that farmers are willing to adopt PA technologies if made accessible. It is therefore advised that legislative tools, training programs, and necessary access to financing facilities be employed to make these technologies available and cheap for farmers.

Existing smart farming technology faces sustainability challenges due to high costs and environmental pollution. This study introduces a novel, sealed smart farming system utilizing misting technology to address these limitations. The system is designed to efficiently use water and nutrients, making it particularly suitable for high-value crop cultivation in urban environments with architectural constraints. Over a one-month experimental period, we monitored the system’s performance in a controlled environment. The methodology included setting up the system and regularly measuring water usage, nutrient delivery, and plant growth metrics. The experimental results showed a significant reduction in water usage compared to traditional methods, with precise control of micronutrient delivery. Additionally, the system’s ability to maintain a consistent sealed environment was demonstrated, which is crucial for optimal plant growth. The system’s portability and space utilization efficiency were also highlighted as major advantages. Furthermore, the system demonstrated potential for cultivation in extreme environments, such as water-scarce regions, by maintaining optimal indoor conditions for crop growth. Challenges such as nozzle clogging and uneven mist distribution were identified, indicating the need for further research in cartridge design and misting methods. Overall, this smart farming technology shows significant promise for enhancing global food security and contributing to sustainable agricultural development by minimizing water usage and optimizing nutrient management.