Diagnosis Of Leaf Disease In Cucurbita Gourd Family Using Machine Learning Algorithms
V. Nirmala1, B. Gomathy2
1V. Nirmala, Assistant Professor, Department of ECE, M. Kumarasamy College of Engineering, Karur, India.
2B. Gomathy, Associate Professor, Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, (Tamil Nadu), India.
Manuscript received on 07 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 6800-6804 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5232098319/19©BEIESP | DOI: 10.35940/ijrte.C5232.098319
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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: Paper The paper presents the implementation of various machines learning approach for the diagnosis of leaf diseases. For analysis data collection were done towards capturing the images of pumpkin leaf affected by different diseases. The pumpkin leaf samples were taken. The samples correspond to the blight and fungal problems like Alternaria, Powdery Mildew Anthracnose, and Yellow Vine Disease etc. The methods for analysis were implemented and tested which are based on time, frequency and statistical approach. For classification machine learning approaches like neural networks, SVM, KNN etc were analyzed. The implementation issues were presented for future work. Keywords: Leaf diseases, blight, yellow vine, neural network, wavelet transform, SVM, classifier, denoising.
Scope of the Article: Machine Learning