Vegetable Plants Disease Detection Using Digital Image Processing on SVM Algorithm

The cultivation of plants and crops has seen rapid growth due to the increasing demands of humans and animals worldwide. Agricultural science has developed various techniques to improve production rates in the agricultural sector. However, farmers often face challenges in protecting their crops from diseases and insects during plant growth, resulting in lower harvests and economic losses. For decades, agricultural scientists have been striving to develop quick and efficient systems for detecting plant diseases and providing timely treatment. However, traditional manual detection techniques are time-consuming. To overcome this, new technologies, including machine learning, especially deep learning, have been introduced into the cultivation system for faster and more efficient plant disease detection. In this study, we propose a model that uses image processing and a Support Vector Machine (SVM) for training the dataset to detect leaf diseases. Our system achieves a high accuracy rate of 94.38 percent. Our research work can assist farmers in increasing crop and fruit production rates while mitigating plant diseases and insect attacks, thereby reducing losses and providing immediate remedies.