Real-Time Tomato Quality Assessment Using Hybrid CNN-SVM Model

Abstract

The current quality assessment for fruits and vegetables relies on subjective human judgment and manual inspection, resulting in inconsistencies and inefficiencies. Due to that, there is a need for a real-time system that can accurately and efficiently assess the quality of fruits and vegetables by analyzing various parameters, such as color, texture, size, and blemishes, to ensure consistency and reduce waste in the food supply chain. This study presents the development of a real-time tomato classification system using a hybrid model that combines convolutional neural network (CNN) and support vector machines (SVM) deployed on the embedded single-board NVIDIA Jetson TX1. The selected CNN model EfficientNetB0 was used for feature extraction and SVM for classification. Notably, the EfficientNetB0-SVM hybrid model demonstrated impressive efficiency, achieving an average accuracy of 93.54% for classifying static tomato images stored in a board into healthy or reject with a testing time of 0.0216 seconds per image. Also, during real-time implementation, the proposed hybrid model attained an average inference speed of 15.6 frames per second (15.6 FPS), with an accuracy of 78.57% in classifying actual tomatoes into healthy or reject. The classification decision was taken based on 5 images for each tomato captured at different angles to ensure the detection of any blemishes from almost all sides of the tomato. The performance of the proposed model outperforms that of the state-of-the-art (SOTA) methods in accuracy, testing time per image, and real-time prediction accuracy.