Rose Plant Disease Detection Using Machine Learning

Farmers are now experimenting and taking new yield. So they are now exploring unconventional crops like flowers. To prevent yield and volume losses in agriculture, it is important to be aware of diseases that affect yield. In case of unconventional yield like flower, the farmers are also not in the position to use their experience or traditional knowledge to identify the diseases. Manually tracking flower diseases is a difficult task that requires a lot of effort, knowledge of various flower diseases, and a time-consuming procedure. Hence there is a requirement for an application to detect the flower disease that examines the visible patterns of flowers without the need for special equipment. The flower diseases can be detected making use of image processing. This includes image acquisition, preprocessing, segmentation, feature extraction, and classification as integral steps in the disease detection process. This study focuses on the identification of various diseases related to rose plant which mainly shows symptoms with respect to leaf and flower.