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Kiwifruit (Actinidia chinensis Planch. ) is highly valued for its nutritional benefits and unique flavor. However, diseases like bacterial canker and soft rot threaten its production, causing significant economic losses. Traditional disease identification methods, which rely on human expertise, are time-consuming and lack scalability. This study utilizes deep learning to enhance kiwifruit disease identification by evaluating eight advanced convolutional neural network (CNN) architectures on real-world field data. Among these, ShuffleNetV2ₓ0₅ proved to be the most effective model. By incorporating advanced optimization strategies, including the AdamW optimizer and OneCycleLR scheduler, the model demonstrated rapid convergence and robust performance, achieving over 99% accuracy within five epochs, with only 1. 37M parameters and 0. 04G FLOPs. Its lightweight design and computational efficiency make it ideal for resource-constrained environments, such as mobile devices. These findings highlight the potential of ShuffleNetV2ₓ0₅ to transform precision agriculture by enabling scalable and efficient disease management in kiwifruit production. Our code and models are available at https: //github. com/zhanglab-wbgcas/kiwifruit-diseases-classifier.
Liu et al. (Wed,) studied this question.