Computer Vision Based Detection And Classification of Tomato Leaf Diseases
T. Gayathri Devi1, A. Srinivasan2, S. Sudha3
1T. Gayathri Devi, Department of ECE, Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam, Tamil Nadu, India.
2A. Srinivasan, Department of ECE, Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam, Tamil Nadu, India.
3S. Sudha, Department of ECE, Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam, Tamil Nadu, India.
Manuscript received on 25 August 2019. | Revised Manuscript received on 05 September 2019. | Manuscript published on 30 September 2019. | PP: 3100-3102 | Volume-8 Issue-11, September 2019. | Retrieval Number: K24930981119/2019©BEIESP | DOI: 10.35940/ijitee.K2493.0981119
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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: Indian Economy mainly determined by the agriculture. Tomato is one of the highest used food crops in India. Due to which detection of disease on tomato plant becomes essential. The manual detection of plant diseases are very complex and high cost. Hence, image processing based detection of plant diseases gives the solution. Disease detection involves the steps like image capturing , various processing steps and classification. Most of the diseases of tomato plant detected at initial stages as they affect leaves first. By detecting the diseases at initial stage on leaves will surely avoid impending loss. The classifier, the classification is performed to classify the healthy and disease affected tomato leaves. Finally, the performance of K-nearest neighbor (KNN) and multi class Support Vector machine (SVM) are compared. The proposed system assured an excellent performance to farmers and researchers in admissible way.
Keywords: Tomato, K – Means Clustering, KNN, Multi-class SVM.
Scope of the Article: Clustering