Detecting A Diseases of Plants Using Machine Learning

To reduce the risks of crop failure due to diseases outbreak, machine learning methods can be implemented. Naked eyes inspection for plant diseases usually based on the changes in color or the existence of spots or rotten area in the leaves. Various researches are going on vigorously in plant disease detection. Identification of plant diseases can not only maximize the yield production but also can be supportive for varied types of agricultural practices. This paper proposes a disease detection and classification technique with the help of machine learning mechanisms and image processing tools, Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recommended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farmers will easily find the diseases based on the early symptoms. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted using contour tracing