Leaf Disease Identification using Convolution Neural Network (CNN)
Reeja.SR1, Pavithra.N2, Pinky.B3, Alfiya Anjum.M4, Sheikh Hammaad5
1Dr. Reeja. S.R, Associate Professor in Computer Science and Engineering Department, Dayananda Sagar University, School of Engineering, India.
2Pavithra N, Currently Pursuing the B.Tech Degree in Computer Science and Engineering Department, Dayananda Sagar University, Bangalore, India.
3Pinky B, Currently Pursuing the B.Tech Degree in Computer Science and Engineering Department, Dayananda Sagar University, Bangalore, India.
4Sheikh Hammaad, Currently pursuing B.Tech Degree in Computer Science and Engineering Department, Dayananda Sagar University, Bangalore, India.
5Alfiya Anjum M, Currently Pursuing B.Tech Degree in Computer Science and Engineering Department, Dayananda Sagar University, Bangalore, India.
Manuscript received on June 16, 2020. | Revised Manuscript received on June 25, 2020. | Manuscript published on July 10, 2020. | PP: 477-481 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I7251079920 | DOI: 10.35940/ijitee.I7251.079920
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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: The tomato plant is the most broadly cultivated produce in India. As the Convolutional Neural Network (CNN) which comes under the field of image classification is performing the progressive work, thus using an approach of deep learning which mainly centers on achieving high accuracy of leaf disease of the tomato plant. Therefore, the main objective of this paper is to acquire more reliable performance in the identification of diseases. Amidst various plant diseases that affect leaf comprise of Late blight, bacterial and viral diseases have been chosen to differentiate infected leaves from that of the healthy leaves includes Late blight, bacterial and viral diseases. As we know, none of the other method has been proposed earlier which helps in detecting plant leaf diseases for the first time. Hence the proposed model is designed in such a way that it effectively identifies specific diseases that affect leaves of tomato plants through the use of a dataset containing about 4000 leaf images. CNN achieves an overall accuracy of 96% without implementing any pre-processing and feature extraction methods.
Keywords: CNN, Leaf, Classification, Leaf disease detection.
Scope of the Article: Classification