Tomato pest and disease detection and recognition method based on deep learning

Abstract

The invention discloses a tomato pest and disease detection method based on deep learning, including a pest and disease detection system platform, a data acquisition unit, a data processing and analysis unit, a deep learning model construction unit, and a YOLOv8 algorithm module. The data acquisition unit, data processing and analysis unit, and deep learning model construction unit are arranged inside the detection system platform. The data processing and analysis unit comprises a backbone feature extraction network model, a feature fusion mechanism, and an AI learning computation module. The YOLOv8 algorithm module is integrated within the extraction network model. The invention improves detection performance for small and densely distributed targets, enhancing detailed feature capture of tomato pests and diseases, improving accuracy. It adopts a lightweight algorithm architecture to rapidly process large data volumes and utilizes deep learning algorithms for learning and prediction, significantly improving operational efficiency and achieving high-precision detection.

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