Application of image processing and transfer learning for the detection of rust disease.

ABSTRACT: Plant diseases introduce significant yield and quality losses to the food production industry, worldwide. Early identification of an epidemic could lead to more effective management of the disease and potentially reduce yield loss and limit excessive input costs. Image processing and deep learning techniques have shown promising results in distinguishing healthy and infected plants at early stages. In this paper, the potential of four convolutional neural network models, including Xception, Residual Networks (ResNet)50, EfficientNetB4, and MobileNet, in the detection of rust disease on three commercially important field crops was evaluated. A dataset of 857 positive and 907 negative samples captured in the field and greenhouse environments were used. Training and testing of the algorithms

SUBMITTER: Shahoveisi F 

PROVIDER: S-EPMC10060580 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature