AI-Driven Tomato Quality Assessment: A Deep Learning Approach for Image-based Defect Detection and Classification
Abstract
Agriculture is crucial to India's economic success. Due to increased demand for premium products, fruit grading is crucial to agriculture. Fruit grading by hand is time-consuming, inaccurate, and inefficient. The automatic grading system cuts processing time and errors. Domestic and foreign markets want tomatoes. Grading tomato fruit requires caution due to its fragility. The research recommends a computer vision-based approach to automatically rate tomato fruits. A strong deep learning method for tomato picture acquisition, feature extraction, and classification is presented in this paper. Using an NVIDIA Jetson TX1 system with an RGB camera, 2400 photos were taken of 600 tomatoes of various shapes, sizes, colors, and flaws. Actual sorting mechanisms were simulated by imaging each tomato from four angles. A Skip-GRU network module bypassed extraneous information and used a self-attention method to improve feature extraction. The modified cheetah optimization algorithm (MCOA) improved feature selection for classification. Using convolutional and bidirectional long short-term memory (LSTM) networks with an effective channel attention (ECA) mechanism, an Adaptive CNN-BLSTM model extracted and prioritized significant information for classification. This method accurately detects tomato flaws and classifies them according to OECD and USDA criteria. The dataset has been made public to enhance AI-driven agriculture applications.