Machine-Learning-Based Approach to Orange Quality Detection and Classification

Oranges are the most used fruits in Pakistan. Ensuring quality in oranges will definitely enhance customer satisfaction, and it will do wonders with respect to marketing. In the old days, this consisted of physical inspection with one’s hands and laboratory tests that were very slow and inaccurate in various cases and whose quality was not admissible for any large-scale application. The projected straightforward and simple method identifies pertinent features such as acidity, vitamin C, texture, and color. We used random forest, decision tree, KNN, and naive Bayes, with which we acquired 91.1%, 93.4%, 92.32%, and 97.67% of the data correctly, where naive Bayes performed the best. These methods induce speed, accuracy, and scalability for quality assurance in oranges and are feasible for use in citrus farming. The future will be focused on developing low-cost approaches for smallholder growers and dealers working towards revolutionizing orange quality testing.