Automated food identification and grading system using image processing in agriculture.

Food production and distribution in the food industry require efficient organization. However, manual grading of agricultural produce faces challenges of subjectivity, inefficiency, and inconsistency. To address these issues, this paper proposes a food identification algorithm that utilizes image processing for agricultural technology to identify the type and grades of food products. The methodology involved capturing images of food produced using an ESP32-CAM camera module connected to an Arduino MEGA 2560 board. The images were processed through algorithms to identify the food produced and grade them based on size. The color of the image was extracted by calculating the Euclidean distance of RGB values in the image. The type of food product was identified using the perimeter and the Euclidean distance of RGB values. Then, the algorithm extracted information about the area from the image and provided the value of the area, which determined the grade of the product. The results and discussions present the test outcomes of the food identification and grading algorithms. The food identification algorithm successfully recognized tomatoes and mangoes from the captured images, achieving high accuracy. The food grading algorithm effectively categorized the produce based on the food product's area into Grade A (Big Food Product) if the food product's area is more than 10,000 pixels, and Grade B (Small Food Product) if it is less than 10,000 pixels. In conclusion, the proposed image processing-based food identification and grading system demonstrated potential in automating the food grading process. It provided reliable results in identifying and categorizing an agricultural produce based on visual attributes. [ABSTRACT FROM AUTHOR]