Tomato Maturity Classification and Yield Estimation for RGB and Multispectral Images

Abstract

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.

Plant phenotyping relevance

RGB・マルチスペクトル画像からトマトの成熟度と収量を推定する画像解析ワークフローが中心で、果実状態および収量という植物形質を直接評価している。

Code and data availability

The paper's tomato image datasets (RGB/multispectral sequences, IRIS-formatted data, MOT test sequences) and trained SVM/KNN/ANN model parameters are paper-specific phenotyping assets, but the Data Availability Statement says they are available only on request and not publicly available due to privacy. No public URL or

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