Plant Disease Classification using Deep Learning Google Net Model
Satwinder Kaur1, Garima Joshi2, Renu Vig3
1Satwinder Kaur, B.E, Department of Electronics and Communication Engineering from Panjab University, Chandigarh, India.
2Garima Joshi, P.H.D., Her area of research, Panjab University, Chandigarh, India.
3Renu Vig, P.H.D., Her area of research, Panjab University, Chandigarh, India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 26 August 2019 | PP: 319-322 | Volume-8 Issue-9S August 2019 | Retrieval Number: I10510789S19/19©BEIESP | DOI: 10.35940/ijitee.I1051.0789S19
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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 have been a major crisis that is disturbing the food production. So there is a need to provide proper procedures for plant disease detection at its growing age and also during harvesting stage. Timely disease detection can help the user to respond instantly and sketch for some defensive actions. This detection can be carried out without human intervention by using plant leaf images. Deep learning is progressively best for image detection and classification. In this effort, a deep learning based GoogleNet architecture is used for plant diseases detection. The model is trained using public database of 54,306 images of 14 crop varieties and their respective diseases. It achieves 97.82% accuracy for 14 crop types making it capable of further deployment in a crop detection and protection application.
Keywords: Deep learning, GoogleNet, Plant Disease Detection
Scope of the Article: Classification