ArticleNPJ science of food2025
High-precision pest and disease detection in greenhouses using the novel IM-AlexNet framework.
Article in NPJ science of food, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
China is the largest producer of greenhouse vegetables, but the closed environment fosters high pest and disease incidence. This study proposes an improved AlexNet (IM-AlexNet) model incorporating ReLU6, batch normalization, and GoogleNet Inception-v3 to enhance pest and disease identification. Experimental results show that the IM-AlexNet model is better than the traditional model in indicators such as Precision, Recall, F1, and MAP. Specifically, its MAP value is 88.91%, which is 10.77, 8.6, and 5.14% higher than the AlexNet, CNN, and YOLO-v7 models, which shows stronger generalization capabilities under small sample conditions. It demonstrates strong generalization, reduced missed detection, and improved target recognition in complex backgrounds. This model offers a valuable tool for greenhouse vegetable growers to monitor pests and diseases intelligently, reduce pesticide use, and improve environmental sustainability. The findings provide a foundation for further research in agricultural pest management.
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