AI-based sensing solutions in the food industry: A review

The exponential growth of artificial intelligence (AI) in the digital world has urged researchers to use it for the betterment of society. The sensing world has also integrated AI with various low-cost, non-invasive sensors to optimise performance for real-time decision-making, thereby improving response accuracy and enhancing the scalability and adaptability of the developed AI models. This paper highlights the integration of various machine learning (ML) algorithms with sensors for food industry products. It demonstrates the mechanisms, capabilities, and advantages of each algorithm with significant examples, tailored to the target application. A classification has been performed using ML techniques commonly associated with food-related sensors. Furthermore, the capability of each of these algorithms to successfully operate on sensors with different sensing mechanisms has also been demonstrated. The influence of AI on the food industry has been demonstrated through specific examples that highlight improved food quality and safety. Finally, the paper also highlights the challenges faced by current AI-based food sensors and possible remedies. • The paper presents a topical review of the use of AI-based models in the food industry. • The use of certain machine learning (ML) models has been demonstrated in relation to the sensing solution in the food industry. • The performance of these models with respect to the food products has been shown. • The emerging trends and considerations in the AI world have been highlighted. • Solutions have been proposed to enhance the utilisation of AI-integrated sensors in the food industry.