The research demonstrates improved pest recognition efficacy in strawberries, highlighting the role of small-sample datasets and attention mechanisms.
Intelligent pest detection has become a critical challenge in precision agriculture. Addressing the challenge of distinguishing between aphids, thrips, whiteflies, beet armyworms, spodopetra frugiperda, and spider mites during strawberry growth, this study establishes a small-sample multi-pest dataset for strawberries through field photography, open-source sharing, and web scraping. This study introduces a channel–space parallel attention mechanism (PCSA) into the UNet architecture. This improved UNet model accentuates pest color and morphology through channel-based attention and emphasizes spatial localization with coordinate-based attention, allowing for the comprehensive integration of global and local pixel information. Subsequently, comparative analysis of several color spaces identified HSV as optimal for pest recognition, with the “UNet + PCSA + HSV” approach achieving state-of-the-art results (IoU, 84.8%; recall, 89.9%; precision, 91.8%).
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Zhao et al. (2025) studied this question.
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