Abstract
Apple leaf diseases occur frequently and have a wide variety, which seriously affects the yield and quality of apples. The real-time and accurate identification of apple leaf diseases is the premise to control the common leaf diseases of apple trees during the whole growth cycle, to improve apple quality, and to ensure high-quality apple output. To improve the accuracy of the apple leaf disease identification model, two improved schemes integrated ResNet18 network is proposed for the classification of apple leaf diseases. According to our experiments, the classification accuracies of the ResNet with CBAM module (ResNet-CBAM) and ResNet18 with random clipping branches (ResNet18-RC) are 95.2% and 97.2% respectively, which all slightly improved the performance of ResNet18. The improved ResNet18 networks boost the accuracy of apple leaf disease classification and can provide a theoretical basis for the prevention and control of apple leaf disease.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
