An Efficient Approach for Identification of Multi-plant Disease Using Image Processing Technique

Abstract

AbstractPlant diseases must be identified to prevent loss of yields and quantities of agricultural products. Plant disease studies mean plant patterns are observable visually. Health monitoring and detection of plant diseases are very important for sustainable agriculture. Manually monitoring plant diseases is very difficult. It requires huge work, expertise in plant diseases and too long to deal with them. Image processing is therefore used to detect diseases of plants. Disease detection includes steps like image capture, image preparation, fragmentation of images, extraction of functions, segmentation and characteristic extraction. Farmers need automatic disease monitoring to improve crop growth and productivity. Manual Identification of plant diseases. Manual disease monitoring is not effective, because old naked eyes require a more time-limited process for expertise in disease recognition, and is therefore ineffective. This paper addresses plant disease detection using computer-aided method like image processing with MATLAB. Results show that 87% diseases identification has generated by proposed system.KeywordsPlant leaf featuresImage disease detectionImage processingClassificationConvolutional network