Intelligent Detection of Disease in Plant Leaf using SVM

Crop diseases are a significant hazard to feed a growing population, detecting the disease of the plant is a difficult task since most of the countries do not have sufficient technical infrastructure to detect the plant disease, timely detection is challenging. The study shows how to diagnose leaf disease using image processing techniques for an agricultural vision system. Crop diseases identification is critical in agricultural growth when monitoring massive areas of crops. There are a number of plant diseases that have the potential to cause economic and societal harm. It is more harmful to production if the ailment is not diagnosed at an early stage. To find the specific disease in image processing aids in the detection and the prevention of that disease. For this type of decision analysis, image processing approaches including machine learning techniques can help to achieve the needed outcome in identifying the crop disease. Device based detection of the plant disease is now achievable due to the tremendous growth of smartphone usage, rapid progress in object recognition with the support of the support and potential outcomes of Artificial Intelligence. The Proposed method developed a deep learning model to identify 10 crop species and 25 diseases using a dataset collected of 25,305 photos of damaged and good leaf surface taken under laboratory circumstances on an operated training dataset, which provides accuracy rate of 99.56 percent, establishing the efficacy of the proposed methodology.