Decision Tree-based Machine Learning Algorithms to Classify Rice Plant Diseases
R. Sahith1, P. Vijaya Pal Reddy2, Satyanarayana Nimmala3
1RSahith*, CSE, CVR College of Engineering, Hyderabad, India.
2Dr.P.Vijaya Pal Reddy, CSE, Matrusri Engineering College, Hyderabad, India.
3Satyanarayana Nimmala, CSE, CVR College of Engineering, Hyderabad, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 5365-5368 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4753119119/2019©BEIESP | DOI: 10.35940/ijitee.A4753.119119
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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: Rice is one of the most important foods on earth for human beings. India and China are two countries in the world mostly depend on rice. The output of this crop depends on the many parameters such as soil, water supply, pesticides used, time duration, and infected diseases. Rice Plant Disease (RPD) is one of the important factors that decrease the quantity and quality of rice. Identifying the type of rice plant disease and taking corrective action against the disease in time is always challenging for the farmers. Although the rice plant is affected by many diseases, Bacterial Leaf Blight (BLB), Brown Spot (BS), and Leaf Smut (LS) are major diseases. Identification of this disease is really challenging because the infected leaf has to be processed by the human eye. So in this paper, we focused on machine learning techniques to identify and classify the RPD. We have collected infected rice plant data from the UCI Machine Learning repository. The data set consists of 120 images of infected rice plants in which 40 images are BLB, 40 are BS, and 40 are LS. Experiments are conducted using Decision tree-based machine learning algorithms such as RandomForest, REPTree, and J48. In order to extract the numerical features from the infected images, we have used ColourLayoutFilter supported by WEKA. Experimental analysis is done using 65% data for training and 35% data for testing. The experiments unfold that the Random Forest algorithm is exceptional in predicting RPD.
Keywords: Rice Plant Disease, J48, Machine Learning, Classification.
Scope of the Article: Classification