Fruit Tree Disease Recognition Based on Residual Neural Network and Attention Mechanism

作     者：Xiedong Song Vladimir Y.Mariano 

作者机构：College of Computing and Information TechnologiesNational UniversityManilaPhilippines School of Computer Science and EngineeringJiNing UniversityJiNing 273155China 

出 版 物：《Journal of Artificial Intelligence and Technology》 (人工智能技术学报（英文）)

年 卷 期：2024年第4卷第2期

页      面：145-152页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：attention mechanism disease recognition of fruit trees ResNet transfer learning 

摘      要：Fruit growing has played a huge role in solving food supply issues in many ***,the yield and quality of fruits can be affected by various diseases,and thus timely and accurate identification of disease conditions is particularly ***,using image recognition and object detection technology to diagnose fruit tree diseases has become a research hotspot in forestry *** neural networks eliminate the preprocessing of manual feature selection and have high recognition ***,it is not easy to train due to the risk of gradient *** order to achieve better recognition effect,this research addresses the problem of applying small-scale data samples through data enhancement and transfer learning,and it optimizes the model by combining the two main attention mechanisms of SE and CBAM with *** experiments,it is found that the CBAM ResNet50 model has the best effect,improving the application performance of the studied model in actual scenarios.