Plant Disease Recognition Model Based on Improved YOLOv5

• Published in: Agronomy (Basel) Vol. 12; no. 2; p. 365
• Main Authors: Chen, Zhaoyi, Wu, Ruhui, Lin, Yiyan, Li, Chuyu, Chen, Siyu, Yuan, Zhineng, Chen, Shiwei, Zou, Xiangjun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Accuracy; Agricultural production; agronomy; Airborne microorganisms; Algorithms; Anthracnose; Artificial intelligence; Classification; Deep learning; Degeneration; Discriminant analysis; EIOU; Hevea brasiliensis; Intersections; InvolutionBottleneck; Light; Machine learning; Methods; Modules; Neural networks; Pathogens; Plant diseases; plant diseases recognition; Powdery mildew; Rubber; Rubber trees; SE module; Target recognition; Vision systems; YOLOv5

To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long...