Avinash A. Utikar
Shivkumar Ankamwar
Aarzoo Chougule
Rohan Aher
Abhishek Giri
- Professor Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India
- Student Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India
- Student Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India
- Student Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India
- Student Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra India
Abstract
Crop disease detection is of key importance because of its role in food safety but infrastructural issues still hamper diagnosis in most regions worldwide. Accurate plant disease identification is essential to secure food, predicting yield decline and managing epidemic outbursts. The advent of digital cameras along with the progress of computer vision technology brings to light the mounting demands for the development of automated disease detection methods in precision agriculture, high-yield plant phenotyping, smart greenhouses integration and so on. The Residual Network (ResNet50) was trained on the image-based dataset containing 54,306 images of crop leaves (ResNet50) and modelled for disease classifier. The test set output accuracy for the ResNet50 architecture is as high as 99.30%, which shows its efficiency. This points to the possibility of the use of ResNet models that already are trained on large-scale image datasets for global crop disease diagnosis. This solution can be used as a balanced way of dealing with crop disease identification so that food security efforts at the global level could be enhanced.
Keywords: Automated Disease Detection, Residual Network (Resnet50), Disease Classification, Accuracy, Large-Scale Image Datasets.
[This article belongs to Journal of Telecommunication, Switching Systems and Networks(jotssn)]
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References
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Journal of Telecommunication, Switching Systems and Networks
| Volume | 11 |
| Issue | 01 |
| Received | May 10, 2024 |
| Accepted | May 20, 2024 |
| Published | June 4, 2024 |