PLANT DISEASE DETECTION USING RANDOM FOREST CLASSIFIER WITH NOVEL SEGMENTATION AND FEATURE EXTRACTION STRATEGY

In agricultural applications, plant disease identification is critical for increasing economic yield. To avoid yield loss, early identification of disease in leaves is critical. Machine learning algorithms can be used to classify diseases at an early stage, allowing farmers to take action to avoid further crop damage. The paper's key contribution is the development of an effective method for tracking plants in order to identify and classify diseases at an early stage. The camera sensor will be used by the machine to capture leaf images in the field. For extracting essential features for classification, a novel segmentation and feature extraction technique is proposed. The disease is classified using the random forest algorithm at the monitoring station. The system's efficiency is measured in terms of detection and classification accuracy.