2026· International Journal of Farm Sciences· 0 citations· 59 references
TL;DR
Overall, AI offers strong potential to improve productivity and strengthen global food security under changing environmental conditions, and Continued investment in research, digital infrastructure and capacity building will be essential to realize the full potential of AI for sustainable agricultural development.
Abstract
Agriculture is undergoing a major transformation driven by artificial intelligence (AI), shifting traditional farming toward a more data-driven and technology-enabled system. This review explores the role of AI in modern agriculture, focusing on its key components, applications, benefits, limitations and future prospects. AI technologies such as machine learning, deep learning, computer vision, natural language processing, robotics, predictive analytics, IoT and cloud-edge computing are increasingly integrated into agricultural systems to enhance decision-making, productivity and sustainability. These technologies enable precision farming, crop monitoring, disease detection, forecasting, automation and supply chain optimization, improving efficiency while reducing resource wastage. However, AI adoption remains limited due to high costs, lack of technical skills, data and infrastructure gaps, regulatory uncertainties and concerns about privacy and adaptability. Additional challenges include farmer resistance and the potential loss of traditional agricultural knowledge, highlighting the need for a balanced integration of technology and human expertise. Looking ahead, advances in autonomous systems, multimodal data integration and adaptive learning are expected to make agriculture more resilient, inclusive and sustainable. Overall, AI offers strong potential to improve productivity and strengthen global food security under changing environmental conditions. Continued investment in research, digital infrastructure and capacity building will be essential to realize the full potential of AI for sustainable agricultural development.
This review synthesizes recent advances in AI driven agriculture, highlighting the evolution of intelligent farming, key methodologies, applications, and future directions and emphasizes the growing importance of multimodal AI, explainable models, edge intelligence, and autonomous systems in building sustainable, climate resilient, and resource efficient farming practices that can strengthen global food security.
Geetanjali Joshi, Parul Gandhi· Soochna Shastra - The Mega J...· 0 citations
The rapid development of artificial intelligence (AI) and digital technologies is driving the transformation in the world of modern agriculture as the new era of data-driven and smart agriculture is emerging. The current review is a compilation of recent papers on AI-driven digital transformation of agriculture and food industries with specific focus on technologies, such as machine learning (ML), deep learning (DL), computer vision, Internet of Things (IoT), big data analytics, and more. The technologies allow real-time monitoring, predictive modelling and intelligent decision support systems to predict crop productivity, soil health, diseases and pests and resource management. In addition, using robotics, autonomous systems and unmanned aerial vehicles (UAVs) is helpful to enhance the efficiency and reduce human involvement. While such initiatives have taken place, there remains a number of challenges such as lack of uniformity and interoperability, cost of investment, cybersecurity and the digital divide, particularly in the developing world. The review also highlights the importance of explainable AI (XAI) solutions, cloud computing and edge computing paradigms in promoting transparency, scalability and acceptance of smart farming tools. Furthermore, emerging technologies (blockchain and digital twins) are explored for improving supply chain transparency and resiliency. The research paper concludes with the need to identify future research opportunities, including the need to build sustainable, scalable and inclusive AI-powered agricultural ecosystems to comply with climate-smart agriculture and global food-security goals.
Pratik Patel· Journal of Global Economics· 0 citations
With the speed of AI development, the agriculture sector has been revolutionized, with intelligent, data-driven farming practices enhancing productivity and sustainability while optimizing the use of resources. Smart farming with AI technology involves the integration of machine learning, deep learning, computer vision, Internet of Things (IoT), unmanned aerial vehicles (UAVs), remote sensing, and cloud computing, all working together to track crop conditions, forecast weather, manage water usage, identify plant diseases, and improve agricultural decision-making. In traditional farming, making important observations and decisions is usually done by hand and based on general knowledge, which results in waste of resources and poor crop results. AI-driven precision agriculture, on the other hand, enables farmers to track crop health in real time and make predictions that help them take proactive measures to optimise quality and reduce expenses in their operations.Precision agriculture, on the other hand, powered by AI allows farmers to monitor and predict crop health, enabling them to take proactive steps to maximise crop quality while minimising operational costs. This study reviewed the state-of-the-art smart farming solutions integrated with AI, specifically aimed at crop improvement. The paper outlines the history of smart agriculture, examines the latest AI-driven innovations in agriculture, offers insights into ongoing research and development challenges, and recommends potential future research opportunities. In addition, it highlights the importance of data analytics, sensor technologies, and autonomous agricultural systems in the context of sustainable food production in a world of growing population and climate change. Research objectives are to develop a conceptual framework based on multiple AI technologies that can be combined in a single smart farming ecosystem to boost productivity, minimize environmental impacts, increase decision making accuracy and to support sustainable agricultural development. The results have shown that smart farming with the use of artificial intelligence is a paradigm.
C. Petri· International Journal of Mod...· 0 citations
The sphere of agriculture is currently experiencing an enormous change fueled by the incorporation of the Artificial Intelligence (AI), Internet of Things (IoT), big data analytics, and new sensing technologies. The challenges to farming by the traditional practices usually include; poor use of resources, unpredictable weather, pests, and poor production. AI-based smart farming will provide data-driven, demonstration-free, and predictive solutions that will improve crop yielding, optimize resource use and enable sustainable and agricultural development. Within this paper, a detailed analysis of AI-based smart farming technologies to improve the crop condition is provided, including smart decision-making, precision agriculture, and real-time detection. The suggested framework is an integration of machine learning algorithms, computer vision, remote sensing, and internet of things with devices that will be used to touch soil, weather condition, and crop growth phases and pests. The prediction of yields, detection of diseases and the optimization of irrigation are reviewed using various AI methodologies including supervised learning, deep learning, and reinforcement learning. A comparative analysis shows that AI-based methods outperform the traditional farming methods in the aspects of productivity, economic efficiency, and environmental sustainability. The implementation challenges and scalability issues, as well as research directions are also addressed in the study. The results show that AI-combinations with smart farming can transform the agriculture sector as the application can lead to better quality of crops, higher yield and sustaining food security the world over due to climatic change and population explosion.
Thomas Fischer, Anna Schmidt· International Journal of Mod...· 0 citations
Artificial intelligence has moved from a peripheral research interest to a central pillar of modern crop and livestock production. This review synthesises recent peer-reviewed literature on the application of artificial intelligence across the agricultural value chain, covering precision farming, crop disease and pest detection, yield forecasting, weed management, agricultural robotics, precision livestock farming, and Internet of Things-enabled resource management. The review also considers the growing use of explainable artificial intelligence and the bibliometric patterns that characterise this rapidly expanding field. Machine learning and deep learning techniques, particularly convolutional neural networks, have delivered measurable gains in disease classification accuracy, yield estimation, and autonomous field operations, while remaining constrained by data scarcity, poor cross-environmental generalisation, and limited interpretability. Precision livestock farming has extended these gains to animal welfare monitoring and reproductive management, and Internet of Things architectures have provided the sensing backbone that links artificial intelligence models to real-time field conditions. Despite substantial technical progress, adoption remains uneven, particularly among smallholder farmers in low- and middle-income regions, owing to infrastructure gaps, cost, and limited digital literacy. The review concludes that artificial intelligence in agriculture has reached a stage of technical maturity in controlled settings but requires further work on model transparency, data standardisation, and equitable deployment before its benefits can be realised at scale. Future research should prioritise lightweight and edge-deployable models, federated and privacy-preserving learning architectures, and closer integration between agronomic domain knowledge and algorithmic design.
H. Saharan, Aditi Mathur, Anubhav Beniwal et al.· Journal of Scientific Resear...· 0 citations
Modern agriculture faces several critical challenges, including unpredictable weather conditions, inefficient resource utilization, and pest infestations, all of which negatively impact crop productivity and sustainability. Traditional farming practices often lack automation and data‐driven decision‐making, leading to increased operational costs, resource wastage, and reduced yields. Furthermore, many existing smart farming solutions remain fragmented, focus on isolated tasks, require high computational resources, and are not well‐suited for resource‐constrained environments, highlighting the need for a more integrated and cost‐effective approach. To address these challenges, this work proposes an IoT‐enabled smart farming system integrating machine learning, deep learning, and automation for agricultural management. The system automates operations such as plowing, sowing, irrigation, fertilization, pesticide, and weedicide application through an Android‐based interface. A Naive Bayes model is employed for crop recommendation based on environmental parameters, enabling data‐driven decision‐making, achieving an accuracy of 96.5%. The system incorporates real‐time soil moisture and temperature monitoring for automated irrigation control, improving irrigation efficiency, resulting in up to 90% improvement in water‐use efficiency compared to conventional methods. Pest detection is performed using a hybrid approach combining Passive Infrared (PIR) sensing and sound analysis, achieving an accuracy of approximately 96%. Weed detection is performed using an Inception‐based deep learning model, achieving a training accuracy of 99.9% and validation accuracy of 99%. Soil health assessment is performed using an optical transducer‐based approach for estimating NPK levels, providing a low‐cost, real‐time solution. The integrated IoT–AI framework improves productivity, efficiency, and sustainability in resource‐constrained agricultural environments.
C. P. Mohanty, Aparna Mohanty, G. Sumathi· Engineering Reports· 0 citations
