Tomato Leaf Disease Detection using Back Propagation Neural Network
1Roopali Gupta, Department of Computer Science Engineering, Rungta College of Engineering and Technology, Raipur, C.G. India.
2Toran Verma, Department of Computer Science Engineering, Rungta College of Engineering and Technology, Raipur, C.G., India.
Manuscript received on May 16, 2020. | Revised Manuscript received on May 17, 2020. | Manuscript published on June 10, 2020. | PP: 529-538 | Volume-9 Issue-8, June 2020. | Retrieval Number: H6531069820/2020©BEIESP | DOI: 10.35940/ijitee.H6531.069820
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: Most of the Indian economy rely on agriculture, so identifying any diseases crop in early stages is very crucial as these diseases in plants causes a large drop in the production and economy of the farmers and therefore, degradation of the crop which emphasize on the early detection of the plant disease. These days, detection of plant diseases has become a hot topic in the area of interest of the researchers. Farmers followed a traditional approach for identifying and detecting diseases in plants with naked eyes, which didn’t help much as the disease may have caused much damage to the plant. Tomato crop shares a huge portion of Indian cuisine and can be prone to various Air-Bourne and Soil-Bourne diseases. In this paper, we tried to automate the Tomato Plant Leaf disease detection by studying the various features of diseased and healthy leaves. The technique used is pattern recognition using Back-Propagation Neural network and comparing the results of this neural network on different features set. Several steps included are image acquisition, image pre-processing, features extraction, subset creation and BPNN classification.
Keywords: Feature Extraction; Image Processing; Tomato Disease Detection; GLCM; BPNN.
Scope of the Article: Image Processing and Pattern Recognition