A Machine Learning-Based Approach for Accurate Size Classification of Pineapple (Ananas Comosus)

Pineapple's size is very crucial in determining its market value. Size sorting is commonly done via visual inspection, which is usually subject to inconsistency and errors. Errors due to failed sorting may either lead to wastage or loss, or mispricing. This study presents incorporation of the machine learning techniques like Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest in classifying pineapple sizes as small, medium, and large using the extracted features of images processed via OpenCV libraries as well as Python Programming. A total of 300 pineapples of different sizes were captured and processed to extract features such as the area, width, height, enclosed-circle radius, and perimeter. The models were optimized using GridSearchCV and were evaluated using accuracy and F1 score metrics. Based on the results, SVM was found to be the most suited classification model, having an optimized training and testing accuracy of 95.67 % and 96.67 %, respectively, and an F1 score of 96.67 %.