Tomayto, Tomahto: A Machine Learning Approach for Tomato Ripening Stage Identification Using Pixel-Based Color Image Classification

The main enterprise of the Philippine agriculture sector is crop cultivation where tomato is deliberated as one of the major crops in the country. With the abundance on tomato production, ripeness classification becomes fairly laborious and challenging, not to mention the subjective visual interpretation of human graders grounded from practical experience that is easily influenced by the environment and prone to error. Thus, this study proposes an automatic tomato ripeness identification using Support Vector Machine (SVM) classifier and CIELab color space via a machine learning approach. Dataset used for modeling and validation experiment in a 5-fold cross-validation strategy was composed of 900 images assembled from a farm and various image search engines. Divided into six classes that represent tomato ripening stages, experimental results showed that the proposed method was successful with 83.39% accuracy in ripeness classification detection. With this machine learning approach and combination of image processing techniques, the agriculture industry could benefit by automating the ripeness estimation which then could save tomatoes from damage.