Machine Learning in Agriculture: A Comprehensive Updated Review

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

Machine learning (ML) has revolutionized resource management in agriculture by analyzing vast amounts of data and creating precise predictive models. Precision agriculture improves agricultural productivity and profitability while reducing costs and environmental impact. However, ML implementation faces challenges such as managing large volumes of data and adequate infrastructure. Despite significant advances in ML applications in sustainable agriculture, there is still a lack of deep and systematic understanding in several areas. Challenges include integrating data sources and adapting models to local conditions. This research aims to identify research trends and key players associated with ML use in sustainable agriculture. A systematic review was conducted using the PRISMA methodology by a bibliometric analysis to capture relevant studies from the Scopus and Web of Science databases. The study analyzed the ML literature in sustainable agriculture between 2007 and 2025, identifying 124 articles that meet the criteria for certainty assessment. The findings show a quadratic polynomial growth in the publication of articles on ML in sustainable agriculture, with a notable increase of up to 91% per year. The most productive years were 2024, 2022, and 2023, demonstrating a growing interest in the field. The study highlights the importance of integrating data from multiple sources for improved decision making, soil health monitoring, and understanding the interaction between climate, topography, and soil properties with agricultural land use and crop patterns. Furthermore, ML in sustainable agriculture has evolved from understanding weather data to integrating advanced technologies like the Internet of Things, remote sensing, and smart farming. Finally, the research agenda highlights the need for the deepening and expansion of predominant concepts, such as deep learning and smart farming, to develop more detailed and specialized studies and explore new applications to maximize the benefits of ML in agricultural sustainability.

Precision agriculture utilizes information technologies to efficiently manage water and soil resources in agriculture. This technological revolution has been sparked by the big data analytics, machine learning and Internet of Things (IoT)in numerous industries. This game-changing technology has influence on houses, grids, smart cities, and health. Machine learning and data-intensive decision-making have been made possible, which has created new possibilities for efficiency and innovation in various fields. In several fields, the convergence of IoT, big data analytics, and machine learning has opened the way for cutting-edge solutions and enhanced results.The application of machine learning in the agricultural sector can increase production quantity and quality to meet increasing food demand. These advancements are transforming current agrarian approaches and generating new opportunities, despite some limitations. This paper presents a systematic appraisal of the role of IoT, big data analytics, and machine learning in precision agriculture, highlighting the potential benefits and challenges in this rapidly evolving field.

The book begins with an introduction to sustainable farming, addressing the pressing need for a farming revolution in response to environmental challenges. It highlights the crucial role of technology, including environmental engineering and machine learning, in transforming agriculture into a more efficient and eco-friendly domain. From soil health management and water resource optimization to renewable energy integration and waste recycling, this book delves deep into the principles of environmental engineering applied to agriculture. One of the most groundbreaking aspects of this book is its exploration of machine learning in agriculture. It provides a user-friendly introduction to AI and predictive analytics for non-experts while covering advanced applications such as disease and pest detection, precision farming, and climate adaptation strategies. The book also discusses image recognition for crop health monitoring and the challenges of implementing AI-driven solutions in the farming sector. Readers will find inspiring success stories showcasing real-world applications of machine learning in modern agriculture. Smart agricultural practices form another pillar of this book, providing an in-depth look at innovative solutions like IoT-based farming systems, automated irrigation, agricultural drones, blockchain for supply chain transparency, robotics, and smart sensors. These technologies are paving the way for a more efficient and data-driven approach to farming, ensuring sustainability while maximizing productivity. A critical aspect of sustainable farming is soil health and fertility management. This book explores sustainable enrichment techniques, bioengineering for soil restoration, AI-driven soil analysis, and best practices such as crop rotation and companion planting. It also warns against the risks of over-fertilization and eutrophication, emphasizing the importance of balance in soil nutrition. Climate-smart agriculture is another vital component, addressing strategies for climate change adaptation, greenhouse gas mitigation, drought-resilient crops, and water management in changing climates. The book highlights the role of AI in climate adaptation and presents policies and success stories from around the world, illustrating effective approaches to climate-resilient farming. Additionally, the book covers sustainable crop management, ethical livestock farming, advanced irrigation techniques, and the economic and social impacts of smart agriculture. It discusses how technology can empower farmers, reduce costs, and address global food security challenges, while also bridging the urban-rural divide through education and training. For those interested in the policy and global perspectives of sustainable farming, the book provides an overview of international initiatives, government incentives, legal and ethical considerations, and collaborations between private and public sectors. It offers case studies demonstrating policy-driven agricultural transformations and insights into the future of farming. The concluding chapter envisions the future of sustainable agriculture, exploring emerging technologies such as AI, IoT, robotics, and big data in farming. It emphasizes the importance of a circular economy in agriculture and highlights the challenges that must be overcome for widespread adoption. Finally, it inspires readers to contribute to a global movement for sustainable farming and food security. With a blend of scientific research, practical applications, and forward-thinking strategies, Sustainable Farming Revolution is an indispensable guide for those seeking to harness the power of environmental engineering, AI, and smart agricultural practices to build a more sustainable and resilient future.

This review paper provides an overview of the applications of machine learning in the agriculture field. Machine learning, a subfield of artificial intelligence, has been successfully applied to various domains, and agriculture is no exception. The paper starts with a brief introduction to machine learning and its various algorithms. It then presents various applications of machine learning in agriculture, including crop yield prediction, precision agriculture, plant disease detection, and soil moisture prediction. The paper highlights the advantages of using machine learning in agriculture, including increased efficiency, reduced costs, and improved decision-making. It also discusses the challenges faced in the application of machine learning in agriculture, including the need for large amounts of data and the difficulty in collecting high-quality data in remote and rural areas. Finally, the paper concludes with future directions for research and the potential impact of machine learning on the agriculture industry. The review shows that machine learning has the potential to revolutionize the way we approach agriculture and food production, leading to a more sustainable and efficient future for the industry.

Machine learning is an emergent field of research in crop production analysis. The present study aims at shedding light on machine learning in agriculture along with “crop management,” “water management,” “soil management,” “fertilizer management.” Production prediction is a very important issue in agriculture. Any farmer wants to know what his expected production is about to expect. In the past, production predictions were made by considering farmers’ experience in specific fields and crops. Based on existing data, performance prediction is a major problem to be solved. Machine learning technology is the best choice for this purpose. Different machine learning techniques are used and evaluated in agriculture to estimate crop productions in the coming year. The aim of this paper proposes and implements a system that can predict crop productions based on previous data. This is achieved by applying machine learning algorithms like Naïve Bayes, generalized linear model, deep learning, decision tree, and random forest to agricultural data and recommending suitable fertilizers for each specific crop and it has been observed that random forest algorithm achieved better accuracy and performance to predict future crop productions.KeywordsAgriculture dataProduction predictionMachine learning algorithmSoil typesFertilizers

Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field.

The growing demand for sustainable food production has driven significant advancements in modern agriculture, including increasing interest in Controlled Environment Agriculture (CEA), a high-tech solution designed to provide fresh, local, and organic products. Although the integration of various technologies in agriculture continues to expand, many opportunities remain to improve environmental performance and operational efficiency. Recent advancements in Remote Sensing (RS) and Machine Learning (ML) offer promising tools for enhancing resource efficiency, improving sustainability, and optimizing processes across various agricultural settings. This study presents a bibliometric analysis of the application of Remote Sensing and Machine Learning in agriculture, highlighting publication trends, influential research contributions, and emerging themes in this interdisciplinary field. While the majority of the analyzed literature addresses general agricultural modernization, the growing relevance of RS and ML in artificial climate facilities and controlled environments has been evident in more recent research. Furthermore, we explore how RS and ML technologies contribute to real-time monitoring, precision agriculture, and decision-making in agriculture.

Machine learning (ML) is rapidly transforming agriculture by introducing data-driven insights and automation into farming practices, enabling precision, efficiency, and sustainability. This paper explores the foundational concepts of ML, distinguishing it from conventional programming approaches through its ability to learn patterns and make predictions from data without explicit instructions. The discussion delves into the applications of ML in agriculture, including crop management, water resource optimization, soil quality assessment, and livestock health monitoring, highlighting its potential to address complex challenges. Furthermore, the paper outlines various ML algorithms, such as decision trees, support vector machines, neural networks, and ensemble methods, emphasizing their suitability for specific agricultural tasks. Despite its promising potential, the adoption of ML in agriculture faces several challenges, including data scarcity, model interpretability, high implementation costs, and limited technical expertise among farmers. This study aims to provide a comprehensive overview of the transformative role of ML in agriculture while critically analyzing the barriers to its widespread adoption. By addressing these challenges, ML can become a cornerstone of sustainable and innovative agricultural practices.

The integration of artificial intelligence (AI) and machine learning (ML) into crop management is transforming modern agriculture by enhancing efficiency, sustainability, and resilience. This review explores the multifaceted applications of AI and ML in key areas such as precision farming, pest and disease management, and harvest optimization. The use of AI-driven predictive analytics allows for more accurate forecasting of crop yields, pest outbreaks, and weather conditions, enabling farmers to make data-driven decisions that optimize resource use and reduce environmental impacts. A significant advancement is the integration of AI and ML with the Internet of Things (IoT) and autonomous farming equipment. These technologies enable real-time monitoring and precise interventions, enhancing productivity and minimizing labor costs. In crop breeding and genomics, AI accelerates the development of resilient crop varieties, which is crucial for adapting to climate change and increasing food demands. Despite the many benefits, challenges such as data quality, infrastructure limitations, and high implementation costs remain. The adoption of AI technologies is uneven, with small-scale farmers in developing regions facing barriers due to limited access to data and resources. Ethical concerns related to data privacy and the digital divide must also be addressed to ensure equitable access to these technologies. The future of AI and ML in agriculture lies in the development of more advanced predictive models, enhanced integration with the IoT, and the widespread use of autonomous farming systems.

In recent years, the application of Machine Learning (ML) in agriculture has emerged as a crucial innovation for enhancing productivity. This systematic literature review (SLR) aims to evaluate the effectiveness of Machine Learning models in the quantitative analysis of crops. Utilizing the PICO methodology and adhering to PRISMA standards, research articles published between 2015 and 2024 were thoroughly analyzed. Results sourced from Scopus highlight that the most commonly employed techniques are Deep Learning, Random Forest, and Support Vector Machine (SVM) due to their high accuracy and capacity to manage large datasets. The review also addresses challenges related to data quality and model implementation, emphasizing the need for ongoing research and international collaboration to advance agricultural technology. The findings underscore the transformative potential of ML in agriculture, paving the way for enhanced crop precision and improved decision-making processes.

The swift advancement of machine learning (ML) has altered several industries, including agriculture, by providing innovative ways of addressing complex challenges related to modern farming. This chapter discusses the use of ML in precision agriculture, emphasizing its capacity to maximize crop output and improve agricultural practices. It studies the use of supervised, unsupervised, reinforcement, and deep learning methodologies to evaluate extensive datasets derived from remote sensing technologies, soil sensors, climate data, and agricultural equipment. Principal applications include predictive modeling for agricultural yield estimation, pest and disease identification, soil health assessment, irrigation optimization, and precision fertilization. The chapter also examines the problems and limits related to the implementation of machine learning in agriculture, including data quality and farmer acceptance.

Machine learning is a promising domain which is widely used now a days in the field of agriculture. The availability of manpower for agriculture is not enough and skill full farmers are less. Understanding the situation of the crop is not that much easy to detect and prevent the diseases in the crop. It is also widely employed in various agricultural fields such as topsoil management, yield management, water management, disease management and climate conditions. The machine learning models facilitate very fast and optimal decisions. The model of machine learning involves with training and testing to predict the accuracy of the result. The use of machine learning in agriculture helps to increase the productivity and better management on soil classification, disease detection, species management, water management, yield prediction, crop quality and weed detection. This article aims at providing detailed information on various machine learning approaches proposed in the past five years by emphasizing the advantage and disadvantages. It also compares different machine learning algorithms used in the modern agricultural field.

Artificial intelligence (AI) is the branch of science that deals with the development of machines to mimic human intelligence. Machine learning (ML) is a subdomain of AI where the machine can learn automatically from data without being explicitly programmed. Agriculture is constantly pressed upon to produce more with less resource. AI and ML techniques have the capacity to optimize resource utilization by analysing agricultural data. It has changed the present-day face of farming by predicting various input parameters and forecasting post-harvest life of a crop. This chapter discusses the different AI and ML techniques available and how they have been used in different phases of the agriculture life cycle. This chapter includes vast range areas in agriculture that requires AI and ML. It includes soil, irrigation, and disease managements. Importance of AI in the field of plant phenomics also included in this chapter. The probable use of geographic information system (GIS) and remote sensing coupled with AI are discussed in this chapter.

The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” and “rice”. Only journal papers were considered eligible that were published within 2020 – 2023. Approximately 49 articles from scopus and google scholar database. It was selected for full content review after the pre-screening process. The results indicated that this topic pertains to different disciplines that favor convergence research at the international level. Prediction criteria related to food security were found to be the most inputs of prediction models. Machine learning models such as Random forest result in high accuracy in predict rice stock on 98%. This study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in predict rice stock and contributing to a more systematic research on this topic.