Disease Recognition in Broccoli: Enhancing Crop Health and Productivity

Because of its high nutritional value and contributions to global food security, broccoli is a crucial crop. However, a number of illnesses can affect its cultivation and have a substantial impact on quality and yield. We offer a unique method for broccoli disease identification utilising a Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model to handle this problem. While the SVM makes the final illness classification, the CNN retrieves key information from leaf pictures. Our model’s overall accuracy was 83.213% after it was trained and tested on a wide range of broccoli leaf image datasets. The outcomes show how well the combined CNN-SVM model performs in distinguishing between damaged and healthy broccoli leaves. By providing a useful tool for early disease diagnosis and efficient crop management in broccoli growing, this research advances precision agriculture.