Segmentation Technique for Soybean Leaves Disease Detection

Soybean is a very essential crop which plays a key role in the complete food chain. But due to the different reasons like diseases, pest attack & suddenly changing weather conditions, the productivity of soybean crop decreases qualitatively and quantitatively. To minimize the loss in productivity of soybean crop the early stage disease detection is required preventive measure. An application of Image processing technique, in the field of agriculture is emerging exponentially which is ranging from the field management to crop disease detection. This paper presents study of soybean leaves colored images for disease detection by inspecting the visual symptoms of particular disease by using segmentation. Soybean is prone to many diseases and traits. To control these diseases farmers are experiencing so much toughness while making the one disease control policy to another. Always insecticides are not more efficient to control diseases, as the diseases on soybean leaves causes major loss in production and economic loss in the soybean crop. Hence to take the corrective action, digital image processing technique is used to detect diseased leaves. This article provides an advanced technique to detect the diseased leaves and normal leaves of soybean crop. In this work the kinds of methods are used to observe and learn the soybean leaves diseases using digital image processing. For image segmentation phenomenon, in this article sobel filter, canny edge detector, prewitt filter, k-means clustering techniques are used. By Using above segmentation techniques we can analyze the diseased crop or normal crop in better way