Wheat Leaf Detection and Prevention Using Support Vector Machine

Abstract

Agriculture is an ancient occupation. Machine learning technique is used for wheat leaf disease detection. Disease is restricting the growth of wheat plant. Quality and quantity of wheat plant is also reduced by it. For color space lab color space is used. Wheat leaf image is captured by the digital camera. After it the captured image is processed to determine the diseased and un-diseased status of each test leaf. To identify the clusters of wheat leaf k-means clustering method is used. The classification technique support vector machine is used to perform action on different wheat leaf samples. Support vector machine contains two datasets; one is training dataset and testing data. Comparison result shows the diseased and un-diseased leaf from the test data. Test results verified by terms; mean, standard deviation, variance, median and mode.