Utilising machine learning and image processing, this computer vision system detects defects and improves tomato quality

Abstract

Computer vision is a technology that integrates image processing and machine learning to evaluate product quality objectively and non-destructively. This study establishes a computer vision system for defect detection and quality assessment of tomatoes by image processing methods and machine learning algorithms, including support vector machine (SVM) and artificial neural network (ANN). The procedure commences with the collection of tomato photos, succeeded by pre-processing, segmentation, and the extraction of features related to colour, texture, and shape. Machine learning models are subsequently utilised to categorise tomatoes according to their ripeness levels and the existence of faults. The results indicate that the system can accurately detect flaws and evaluate the quality of tomatoes, demonstrating superior efficiency compared to manual techniques. This method is anticipated to enhance the uniformity of quality standards and diminish waste in the agriculture sector.