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

Daniel Chang

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

Author(s)	Daniel Chang
Country	India
Abstract	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 (Upadhyay et al., 2025; Agrawal & Arafat, 2024; Upadhyay et al., 2025; Mohanty et al., 2016; Ferentinos, 2018; Liakos et al., 2018). 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 minimise the loss of yield (Mohanty et al., 2016; Ferentinos, 2018). Additionally, the analysis has highlighted the crucial shortcomings of these AI-based techniques (Upadhyay et al., 2025; Majdalawieh et al., 2025). 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 following responsible use of artificial intelligence. This can be achieved to ensure that its application is more susceptible for a wider portion of the population.
Keywords	Artificial Intelligence; Digital Agriculture; Precision Agriculture; Machine Learning Applications; Food Security; Sustainable Agriculture; Agricultural Data Analytics; Computer Vision Farming; Deep Learning; Crop Disease Detection; UAV Drone Imaging; Remote Sensing; Soil Health Monitoring; Irrigation Management; Yield Prediction; Pest Detection; Early Disease Diagnosis; Sustainable Food Systems; Agricultural Robotics; AI Driven Supply Chain Management; Field-Based AI Testing
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-20

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

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