Abstract
A simple Convolutional neural network (CNN) based deep learning (DL) model has been proposed for multi-classification of corn gray leaf spot (CGLS) disease based on five different severity levels of CGLS disease on the corn plant. Certain corn leaf diseases like CGLS, common rust, and leaf blight are quite common and dangerous in corn harvest. Hence, the current work presents a solution for CGLS disease detection on corn plants using a multi-classification DL model which gives the best detection accuracy of 95.33% in high-risk severity level image. Along with this comparison of five different severity levels has also been conducted based on resulted performance measures (PM).
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Claims 1
Claim · model-written summary
The proposed CNN detected corn gray leaf spot severity with a best accuracy of 95.33% in the high-risk severity level images.
- Taxon
- corn
Zea mays subsp. mayse0 - Outcome
- detection accuracye1
- Value
- 95.33%e1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“CGLS disease detection on corn plants”
e1“which gives the best detection accuracy of 95.33% in high-risk severity level image”