Artificial Intelligence and Machine Learning in Agriculture: Transforming Toward Smart, Sustainable and Resilient Farming Systems: A Review

Agriculture is facing global issues such as population growth, climate change and a lack of resources. Artificial Intelligence (AI) and Machine Learning (ML) have the potential to transform with data-driven optimization in crop and livestock management and supply chains. This review collates literature regarding the use, advantages and disadvantages of AI/ML and provides consideration of the socio-economic and ethical implications. AI/ML technologies, especially CNNs for image analysis and LSTMs for time-series prediction, are transforming the farming industry. The benefits are significant such as reducing water consumption by up to 40% with the use of smart irrigation and chemical usage by up to 90% with precision pest/weed detection. AI can also be used to improve livestock welfare and optimize supply chain logistics by forecasting and quality controlling. AI and ML provide unprecedented precision and sustainability in agriculture. Emerging technologies such as Explainable AI (XAI), Digital Twins and Generative AI, despite the challenges of cost, data infrastructure and ethical considerations, are expected to be key to future innovation and global food security.