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

This research paper has focused on the application of artificial intelligence to enhance agricultural productivity and food security, by conducting a qualitative literature review of seven peer-reviewed articles published between the years 2016-2025. The study has investigated the use of machine learning, deep learning, computer vision and remote sensing technologies in regards to crop disease detection, precision farming and decision support systems. The results demonstrate that AI-based techniques are always better compared to the manual monitoring of crops for disease because they allow earlier diagnosis of crop stress and disease, making resources more efficient and minimising the loss of yield. Additionally, the analysis has highlighted the crucial shortcomings of these AI-based techniques. This included the use of controlled datasets, a lack of real-world validation and model-transparency. By analysing the cost-benefit aspect, this paper has stressed that although artificial intelligence has an immense potential in improving food security and sustainability, there is a need to apply more field tests, standardisation and responsible use of artificial intelligence.