Early Detection of Plant Disease using Machine Learning
D.Femi1, T.Danush Chowdary2, Nallanukala Anilkumar3
1D FEMI, Assistant Professor in the Department of Computer Science and Engineering at Vel Tech Rangarajan. Dr. Sagunthala R&D Institute of Science and Technology, Chennai, (Tamil Nadu), India.
2T. Danush Chowdary, pursing his B.Tech BE degree in Computer Science and Engineering from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, (Tamil Nadu), India.
3Nallanukala Anilkumar, pursing his B.Tech BE degree in Computer Science and Engineering from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Tec hnology, Chennai, (Tamil Nadu), India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 950-952 | Volume-8 Issue-8, June 2019 | Retrieval Number: G5664058719/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: India’s economy highly depends on agricultural yield and it is somewhat on which economy greatly depends. In agricultural field implementing computer technology is a difficult task because still most of the major tasks are done manually. Disease detection in plant life plays a significant part in cultivation field which enhances the quality and production. The proof of identity of infection happening in the plant may be a vigorous main to stop an important damage of harvest and also the amount of farming goods. The indications are often ascertained on the essentials of the plant life like leaf, shoots, and fruitlets. The leaf shows lesions like change in colour, spots etc. This recognition is done by manually which can consume more time and it’s costly because it requires more man power. To avoid manual method we are going to automate a system which identifies the plant disease as soon as they appear on the leaf, stem and fruits. A Gradient Anisotropic diffusion Image Filter is used for pre-processing, segmentation is done using Region based segmentation algorithms, feature values are extracted using GLCM and recognizing the disease is done using support vector machines.
Keyword: Gradient anisotropic diffusion, Region based, Gray-level co-occurrence matrix, Support vector machine.
Scope of the Article: Machine Learning.