Deep learning to identifying food maturity for prevent food waste

The article discusses the issues of malnutrition and food insecurity worldwide, high lighting the significant problem of food waste. We propose a technological solution to address this specific issue. The use of computer vision, particularly Deep Learning (DL) models, is proposed as a means to identify the maturity state of food, thereby deter mining its consumability and preventing premature waste. This manuscript explores various applications of deep learning in topics related to the food industry, focusing on research aimed at mitigating food waste. Three datasets were constructed to identify the most commonly wasted foods, as well as to determine the maturity levels of these foods. A deep learning model was developed to identify three different food items with an effectiveness rate of 77%. Additionally, three deep learning models were created to assess the maturity degree of each food, achieving an average effectiveness of 70%. These results are comparable to similar studies that have employed deep learning models. This research shows the feasibility of using deep learning models to determine the maturity levels of three types of food that are frequently wasted in México. This approach is novel in research areas dedicated to developing methods, techniques, or technologies for avoiding or reducing food waste.