Detection of plant leaf disease using machine learning

The essential part of any ecosystem is plant. All the organisms get energy from plants directly or indirectly. It is important to identify the disease in plant parts like leaf, stem, and fruit. Leaf diseases are caused by viruses, bacteria, etc. Normally, a farmer identifies the leaf disease by observing spots, color, and shape of the leaf, but sometimes they take help from the experts to detect diseased leaf or crops. The manual detection of disease i.e. In the segmentation part the RGB image is converted into HSV and the green colors less accurate and complex. The main motivation of this approach is to distinguish the plant leaf is healthy or not. This method has four parts i.e. image pre-processing, segmentation, feature extraction, and classification of diseases using a machine learning algorithm. The pre-processing of the images contains the RGB to Gray conversion and the background filter is used to remove the existing noise in the image. The mask is applied to take out the interesting part and remove the background. The various features of leaf-like shape, color, and texture which may be used to determine the superiority extracted features from the image. SVM algorithm is used for the classification of healthy and diseased leaves. The disease detection till now was manual, but now using machine learning it is going to be very helpful for the farmers to classify the diseased and healthy leaves of plants like Apple, corn, grape, and tomato. For future scope, the mobile application can be developed for the image capturing and detecting the diseased leaves and the fertilizers used can also be displayed in this mobile application which will be very helpful for the farmers.