The Effectiveness of Using Artificial Intelligence in Agriculture: The Implications that Artificial Intelligence Can Have on Agriculture and Food Security | RISE Research

This paper evaluates the effectiveness of artificial intelligence in agriculture and its implications for food security, arguing that AI-based methods can improve food security more effectively than conventional approaches. It is structured as a qualitative literature review of seven peer-reviewed articles published between 2016 and 2025, examining the application of machine learning, deep learning, computer vision and remote-sensing technologies to crop-disease detection, precision farming and decision-support systems. The review situates AI within the wider crisis facing global agriculture, including widespread undernourishment, climate change, soil erosion and degradation, over-tillage and deforestation, all of which threaten the capacity to feed a growing population. Against this backdrop, it finds that AI-based techniques consistently outperform manual crop monitoring by enabling earlier diagnosis of crop stress and disease, using resources more efficiently and reducing yield loss. At the same time, the paper is careful to highlight the shortcomings of current AI agricultural systems: reliance on controlled or idealised datasets, a lack of real-world field validation and limited model transparency. Through a cost-benefit lens, it argues that while AI holds immense potential for improving food security and sustainability, realising that potential at scale requires more field testing, standardisation and responsible deployment so the benefits reach a wider population rather than only well-resourced operations. The paper's focus spans precision and digital agriculture, crop-disease detection, yield prediction, soil-health monitoring and agricultural robotics, and its contribution is a balanced synthesis that affirms AI's advantage over traditional methods while insisting on the practical conditions needed for trustworthy, equitable real-world use.