Jul 2026· Soochna Shastra - The Mega Journal of Information Technology· 0 citations
TL;DR
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.
Abstract
Artificial Intelligence (AI) is rapidly becoming a transformative force in agriculture, enabling smarter, data driven decisions across the entire production cycle. By combining machine learning, deep learning, computer vision, remote sensing, IoT, robotics, and cloud computing, AI has accelerated precision farming practices. These advances have improved crop monitoring, disease diagnosis, yield prediction, irrigation management, nutrient optimization, and even autonomous farming systems (Wolfert et al., 2017; Liakos et al., 2018; Kamilaris & Prenafeta Boldú, 2018; Benos et al., 2021). Recent breakthroughs in transformer architectures, multimodal learning, explainable AI (XAI), digital twins, and foundation models have strengthened AI’s ability to integrate diverse datasets from satellites, UAVs, weather stations, sensors, and farm records for real time decision support (Basso & Antle, 2020; Shahhosseini et al., 2021; Dainelli et al., 2022). Compared with traditional statistical methods, AI consistently delivers superior results in yield forecasting, disease detection, irrigation planning, weed identification, and resource optimization by modelling complex interactions among climate, soil, and management factors (Khaki et al., 2020; Talaviya et al., 2020; Feng et al., 2021). However, challenges remain. Limited access to high quality datasets, difficulties in transferring models across regions, computational demands, lack of transparency, data governance concerns, and socioeconomic barriers continue to slow adoption (Benos et al., 2021; Guidotti et al., 2018; UNESCO, 2021). This review synthesizes recent advances in AI driven agriculture, highlighting the evolution of intelligent farming, key methodologies, applications, and future directions. It 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.
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.
D. Sharma· International Journal of Far...· 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
This review aimed to synthesize current advances in artificial intelligence (AI) and intelligent digital platforms applied to animal production, highlighting their main techniques, applications, challenges, and future perspectives. Recent developments in digital agriculture have accelerated the adoption of AI-based tools capable of supporting data-driven decision-making in livestock systems. Among the available technologies, Machine Learning, Deep Learning, and Computer Vision have become the most established approaches for predicting productive performance, monitoring animal health and welfare, optimizing nutrition and reproduction, and improving management efficiency. More recently, intelligent platforms integrating AI, the Internet of Things, cloud computing, and sensor networks have enabled continuous monitoring and real-time decision support across multiple production systems. Emerging technologies, including large language models and explainable AI, are expected to further enhance knowledge management and decision support, although challenges related to interoperability, data quality, model validation, and producer adoption remain. Overall, AI is no longer an emerging technology but an essential component of Precision Livestock Farming, with increasing potential to promote more efficient, sustainable, and evidence-based animal production.
Maíse dos Santos Macário, Isis Regina Santos de Oliveira· Revista Ibero-Americana de H...· 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
Smart agriculture is one such technology that has come as a paradigm shift in a quest to deal with increased food security issues, climate changes and scarcity of resources. When the method of machine learning (ML) is integrated in the agricultural system, it allows making intelligent decisions, predictive analytics, and optimising farming processes. The following paper is a comprehensive study of machine learning-based optimization in smart agriculture based on data-driven solutions to the prediction of crop yields, irrigation timing, soil health, pest and disease detection, and resource management. The proposed framework uses supervised, unsupervised, and reinforcement learning frameworks to maximise agricultural production, reduce the environmental costs and operational cost at a minimum level. The modular methodology is proposed which includes the data acquisition using the IoT-enabled sensors, preprocessing of the data, feature engineering, model training, and real-time deployment. The standard evaluation metrics like accuracy, precision, recall, RMSE, and F1-score are used to compare the performance of the two results of the analysis. The findings show that yield prediction using the hybrid algorithm is significantly more accurate, economical in water consumption and increases the early warning of diseases than conventional rule-based and statistical models. This paper identifies the importance of machine learning as a key to the creation of sustainable, resilient, and scalable agricultural systems and informs about the future research directions in the area of intelligent farming ecosystems.
Moussa Camara· International Journal of App...· 0 citations
Modern agriculture is experiencing a technological revolution through the integration of sensors and artificial intelligence. These automate critical agricultural operations, including harvest management, crop health assessment, and yield forecasting. At the forefront of this transformation is the Digital Twin Living Laboratory (DTLL) technology, which creates virtual replicas of farms that use real-time sensor networks to improve agricultural decision-making through predictive analytics. The DTLL framework works by deploying comprehensive sensor networks across agricultural environments to capture detailed field and plant data. Advanced data processing algorithms analyze this information to predict and prevent adverse conditions, with the Agriculture Internet of Things (AIoT) serving as the technological backbone of DTLL systems. IoT-based monitoring networks use various types of sensors and imaging systems to track key agricultural parameters, including leaf coloration, ambient and soil moisture levels, and temperature fluctuations. The research presented in this article focuses specifically on viticultural applications, where the technology addresses the critical challenge of preventing fungal and bacterial infections that threaten grape production. By implementing IoT-based monitoring systems in viticultural environments, the study aims to develop predictive models that can identify conditions favorable to the development of pathogens, enabling proactive intervention strategies to protect crop health and maintain production quality. A comparative study of data from leaf and plant sensors with leaf images for the identification of infections and their early prevention is performed.
M. Hnatiuc, M. Paun, Domnica Alpetri et al.· Advances in 3OM: Opto-Mechat...· 0 citations