Transfer Learning-Based Approach for Tomato Quality Classification Using Machine Learning Classifiers

Tomato quality assurance is key to improving agricultural output and customer happiness. The study proposes a novel transfer-learning-based strategy for tomato quality classification by integrating deep learning with the more conventional machine learning method. This approach is based on pre-trained convolutional neural network (CNN) models to capture abstract visual representations and to extract characteristics, which are then classified using ML methods such as k-Nearest Neighbor (kNN), Support Vector Machine (SVM), and Random Forest (RF). The proposed method does not require any processing resources or large-sized data to obtain better classification performance. Experimental assessments on a publicly accessible tomato dataset indicate the usefulness of the technique since SVM outperformed other classifiers in terms of accuracy, precision, recall, and F1-score. The hybrid technique, therefore, stands as an economical and scalable technique for the automation of quality testing of tomatoes, an important contribution to the agricultural industry. Future studies would focus on the optimization of models for better performance and real-time applications.