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Open Access
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Avinash A. Utikar, Shivkumar Ankamwar, Aarzoo Chougule, Rohan Aher, Abhishek Giri
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- Professor, Student, Student, Student, Student Department Of Information Technology, Sinhgad College of Engineering, Pune, Department Of Information Technology, Sinhgad College of Engineering, Pune, Department Of Information Technology, Sinhgad College of Engineering, Pune, Department Of Information Technology, Sinhgad College of Engineering, Pune, Department Of Information Technology, Sinhgad College of Engineering, Pune Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra India, India, India, India, India
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Abstract
nCrop 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.
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Keywords: Automated Disease Detection, Residual Network (Resnet50), Disease Classification, Accuracy, Large-Scale Image Datasets.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Telecommunication, Switching Systems and Networks(jotssn)]
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References
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[1] Mr. P V Vinod, Mr. Ramachandra Hebbar, Shima Ramesh (2018). “Plant Disease Detection Using Machine Learning.” MVJ college of Engineering, Bangalore, India.
[2] Lili Li, Shujuan Zhang, And Bin Wang (2021). “Plant Disease Detection and Classification by Deep Learning—A Review” Shanxi Agricultural University, Jinzhong 030800, China.
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[4] Andrew J., Jennifer Eunice, Daniela Elena Popescu (2022). “Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications”. Faculty of Electrical Engineering and Information Technology, University of Oradea, 410087 Oradea, Romania.
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Journal of Telecommunication, Switching Systems and Networks
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | May 10, 2024 | |
| Accepted | May 20, 2024 | |
| Published | June 4, 2024 |
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