Authors: Jigarkumar Ambalal Patel, Mayur Girish Taunk
Abstract: The food industry is one of the largest global employers, yet it faces ongoing challenges in demand–supply chain management and food safety due to heavy reliance on manual processes and human error. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being adopted to transform the industry across the entire "farm to fork" pipeline by improving efficiency, accuracy, and safety. This paper reviews key AI- and ML-driven applications, including smart farming for crop monitoring and yield optimization, automated product sorting and grading, electronic noses for spoilage detection, and vision-based dietary assessment. Despite these advances, significant challenges remain, such as inaccurate image segmentation, high intra-class variation in food appearance, and the lack of large, standardized datasets. Overcoming these limitations is crucial for enabling reliable and scalable real-world deployment of AI.
DOI: http://doi.org/10.5281/zenodo.18359803
