Tomato Maturity Classification and Yield Estimation for RGB and Multispectral Images

With the increasing cost of labor, smart agriculture has emerged as a key trend for the future of agricultural development. This paper presents an integrated approach for tomato maturity clas-sification and yield estimation using both RGB and multispectral images. The proposed approach consists of three main components: tomato detection, tomato tracking and counting, and maturity classification of tomatoes. YOLOv8 combined with OSNet is first employed to detect tomatoes, while StrongSORT is then adopted to track consistent identities across image sequences. For maturity classification, multiple vegetation indices, including NDVI, GNDVI, and GRRI, are first transformed using principal component analysis, followed by classification using support vector machines, k-nearest neighbors, and neural networks. Tomatoes are categorized into three ma-turity levels: immature, almost mature, and mature. Results demonstrate that the proposed ap-proach can effectively estimate yield of tomatoes at each maturity stage. This capability provides practical support for harvest planning and labor allocation in precision agriculture.