Tomato Maturity Classification and Fruit Counting Based on RGB and Multispectral Images

Accurate monitoring of tomato maturity and fruit number is essential for improving crop management and supporting accurate yield estimation in greenhouse environments. However, variations in lighting conditions, occlusions, and overlapping fruits often make reliable maturity classification and fruit counting challenging. This paper presents an integrated approach for tomato maturity classification and fruit number estimation using RGB and multispectral images. The proposed approach consists of tomato detection, tomato tracking and counting, and maturity classification of tomatoes. The detection model identifies tomatoes in each frame, the tracking module associates individual tomatoes across image sequences to avoid duplicate counting, and the classification models determine maturity levels. Experiments are conducted on three tomato datasets collected in greenhouse environments. The results show that the proposed method achieves a maximum maturity classification accuracy of 81%. In addition, the proposed approach facilitates consistent fruit counting across image sequences, supporting practical applications in greenhouse monitoring. These findings demonstrate the potential of integrating RGB and multispectral information for automated tomato maturity classification and fruit counting in precision agriculture.