Plant Disease Detection and Localization using GRADCAM
Ninad Shukla1, Sushila Palwe2, Shubham3, Mohit Rajani4, Aaryan Suri5
1Ninad Shukla , Bachelor’sEngineering, MIT College of Engineering, Pune.
2Sushila Palwe, Assistant Professor in MIT World Peace University.
3Shubham, Bachelor’s in Engineering from MIT College of Engineering, Pune.
4Mohit Rajani, Bachelor’s in Engineering from MIT College of Engineering, Pune.
5Aaryan Suri, Bachelor’s in Engineering from MIT College of Engineering, Pune.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3069-3075 | Volume-8 Issue-6, March 2020. | Retrieval Number: E6935018520/2020©BEIESP | DOI: 10.35940/ijrte.E6935.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Plant diseases are diseases that change or disrupt its important functions. The reduction in the age at which a plant dies is the main danger of plant diseases. And farmers around the world have to face the challenge of identifying and classifying these diseases and changing their treatments for each disease. This task becomes more difficult when they have to rely on naked eyes to identify diseases due to the lack of proper financial resources. But with the widespread use of smartphones by farmers and advances made in the field of deep learning, researchers around the world are trying to find a solution to this problem. Similarly, the purpose of this paper is to classify these diseases using deep learning and localize them on their respective leaves. We have considered two main models for classification called resnet and efficientnet and for localizing these diseases we have used GRADCAM and CAM. GRADCAM was able to localize diseases better than CAM.
Keywords: Plant Leaf Diseases , Deep Learning, Efficientnet , GRADCAM..
Scope of the Article: Deep Learning.