Multilayer Convolutional Neural Network for Plant Diseases Detection
Divya Verma1, Gurpreet singh2, Hatesh Shyan3
1Divya Verma*, Research Scholar, Department of Computer Science Engineering, Chandigarh University, Punjab, India.
2Gurpreet Singh, Assistant Professor, Chandigarh University, Punjab
3Hatesh Shyan. Assistant Professor, Department of Computer Science & Engineering, Chandigarh University, Pujab, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 30, 2020. | Manuscript published on June 30, 2020. | PP: 63-67 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9290069520/2020©BEIESP | DOI: 10.35940/ijeat.E9290.069520
Open Access | Ethics and Policies | Cite | Mendeley
© 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 causing a significant loss in the agriculture production. The diseases infect the plant leaves, stem and the fruits which cannot be utilized. The main causes of plant diseases are bacteria, fungi and virus. The identification and diagnosis of the diseases are necessary. Many researchers have delved deeper in this field and find suitable techniques to this end. Moreover, these days, Convolution Neural network has attracted the interest of the researchers as it gives better results for image processing. This paper presents a comparative analysis of the various approaches designed to diagnose the diseases in different plant at the initial stage so that preventive measure can be taken to enhance the productivity. Along with this, the role of CNN in detecting the disease in the plants is also described in this paper.
Keywords: CNN, plant disease detection, neural network, feature extraction.