A Study on Crop Disease Detection of Banana Plant using Python and Machine Learning
Satyamedha Hosur1, Praveen Banasode2, Minal Patil3
1Satyamedha Hosur*, Department of MCA. Jain College of Engineering Belagavi, Karnataka, India.
2Praveen Banasode, Department of MCA. Jain College of Engineering Belagavi, Karnataka, India.
3Minal Patil, Department of MCA. Jain College of Engineering Belagavi, Karnataka, India.
Manuscript received on September 05, 2020. | Revised Manuscript received on September 25, 2020. | Manuscript published on October 10, 2020. | PP: 278-281 | Volume-9 Issue-12, October 2020 | Retrieval Number: 100.1/ijitee.L80181091220 | DOI: 10.35940/ijitee.L8018.1091220
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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 or leaf disease detection using Python and Machine learning application is designed by using image processing technique for the purpose of farmers to identify, analyze and classify automatically through the computer vision and machine learning vision system for mainly banana leaf to find diseases and by plotting the graph for their pixel range of the affected areas. Leaf diseases are restricting the growth of the plants and it is also destroying the crop. Disease can be controlled by knowing which disease is destroying the plant. The symptom of the banana diseases will be noticed in the leaf, by change in color to yellowish and turning to a dark color and this can be observed between the fourth and fifth month of the plant. Causing reduction in the growth of the plant as well as rotting of the banana. The support vector machine (SVM) algorithm is used for extraction of color and texture features. The proposed work attains a high accuracy in identification of diseases and thereby controlling the spread in other plants.
Keywords: Banana Plant Disease, Banana, leaves, Machine Learning, Support Vector Machine (SVM).