Design And Development Of Loquat Pest And Disease Identification System Based On Attention Mechanis

China is the main producer of loquat,which is a common crop.It can be used as both a medicinal herb and a fruit,making it highly economically valuable.However,loquat diseases and pests seriously affect the growth and quality of fruit.Artificial identification of disease and pest types requires technical personnel with long-term experience and can easily lead to misjudgment,leading to spraying useless pesticides.It not only affects the prevention and control of diseases and pests,but also causes environmental pollution.Therefore,this study utilizes deep learning technology to design a loquat disease and pest recognition algorithm based on convolutional neural networks and attention mechanisms.The CBAM attention mechanism is incorporated into the classic ResNet50 model to further improve the accuracy of convolutional neural networks in image classification.the main work of the paper is as follows:Introducing CBAM based on the traditional ResNet50 model enhances the model’s attention to pest and disease locations,and improves the network’ s recognition accuracy.In the case of a small scale of loquat pest data set,data augmentation and transfer learning are used to improve the speed of model training and generalization ability.In order to verify the actual effectiveness of the model proposed in this paper,it was compared with the basic model ResNet50 and the SE-ResNet50 with channel attention mechanism.The experimental results on the standard dataset Cifar10 showed that the model proposed in this paper has improved compared to the original ResNet50 model.Conduct requirement analysis on the system,design and implement an attention mechanism based identification system for loquat pests and diseases.The system is based on the B/S architecture,and the entire system is built using the Django development framework and SQLite database to achieve online identification of loquat pests and diseases.The system provides an experience exchange platform for loquat planting farmers,facilitating communication and exchange among individual farmers.