Disease Identification in Chilli Leaves using Machine Learning Techniques
Sufola Das Chagas Silva Araujo1, V S Malemath2, K. Meenakshi Sundaram3
1Sufola Das Chagas Silva Araujo, Head, Department of Padre Conceicao College of Engineering, (Goa), India.
2Dr. V S Malemath, Professor, Department of Computer Science and Engineering, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi (Karnataka), India.
3Dr. K. Meenakshi Sundaram, Graduate, Department of Information and Communication Engineering, Studies and Research, Botswana Southern Africa.
Manuscript received on 16 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 325-329 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10611291S319/19©BEIESP | DOI: 10.35940/ijeat.A1061.1291S319
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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: Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.
Keywords: Component, Formatting, Plant, Symptoms.
Scope of the Article: Machine Learning