Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf diseases and insect pests, a novel identification model based on an improved ResNet-50 architecture was proposed, which incorporated the coordinate attention (CA) module and weight-adaptive multi-scale feature fusion (WAMFF) to enhance the ResNet-50’s image feature extraction capabilities. Transfer learning and online data enhancement are employed to boost the model’s generalization ability. The proposed model achieved a top-1 accuracy rate of 98.32% on the basis of AppleLeaf9 datasets, which is 4.58% higher than the value from the original model, and the improved model can effectively improve the localization of lesion features. Furthermore, compared with mainstream deep networks, such as AlexNet, VGG16, DenseNet, MNASNet, and GoogLeNet on the same dataset, the top-1 accuracy rate increased by 7.3%, 3.19%, 4.98%, 6.04% and 3.87%, respectively. The experimental results demonstrate that the improved model is effective in improving the identification accuracy of apple leaf diseases and insect pests and enhancing the model’s effective feature extraction capabilities.

With the aim of increasing the model accuracy of lithium-ion batteries (LIBs), this paper presents a complex-order beetle swarm optimization (CBSO) method, which employs complex-order (CO) operator concepts and mutation into the traditional beetle swarm optimization (BSO). Firstly, a fractional-order equivalent circuit model of LIBs is established based on electrochemical impedance spectroscopy (EIS). Secondly, the CBSO is used for model parameters’ identification, and the model accuracy is verified by simulation experiments. The root-mean-square error (RMSE) and maximum absolute error (MAE) optimization metrics show that the model accuracy with CBSO is superior when compared with the fractional-order BSO.