AI Tools, Techniques, and Technologies in Agriculture

Agriculture is under pressure from climate change, limited resources, and increasing food demand. Artificial intelligence (AI) offers practical solutions by improving precision, efficiency, and sustainability in farming. This chapter reviews key AI tools such as machine learning, deep learning, and computer vision, supported by technologies, including sensors, IoT, GIS, GPS, drones, big data, and ICT. Applications cover the full agricultural value chain, from land use planning and resource management to pest detection, yield forecasting, robotics, postharvest handling, and market integration. Case studies show progress in crop monitoring, disease diagnosis, irrigation management, and autonomous machinery. Hybrid models that combine process-based simulations with AI are also discussed for better predictions. Challenges such as high costs, weak infrastructure, poor data quality, and ethical issues persist. The review concludes that AI can drive sustainable and climate-smart agriculture if accessibility, scalability, and farmer-oriented adoption are ensured.