Rose Plant Disease Detection Using Image Processing and Machine Learning

The first step in preventing reductions in agricultural product output and quantity is to identify plant diseases. The research on plant diseases refers to examinations of patterns on the plant that may be observed with the naked eye. A vital component of sustainable agriculture is the observation of plant health and the identification of disease. The manual monitoring of plant diseases is highly challenging. It necessitates a huge amount of work, knowledge of plant diseases, and lengthy processing times. So, by taking photos of the leaves and comparing them to data sets, image processing is utilized to find plant illnesses. It is incredibly challenging to physically screen plant sicknesses. It requires a colossal measure of work, information on plant illnesses, and extended handling times. In this research, the diagnosis of rose plant diseases is critical for preventing yield and quantity losses in agricultural products. Plant disease identification is crucial for long-lasting agriculture. It requires a significant amount of work, a specialist understanding of plant diseases, and more than enough processing time. As a result, digital image processing is utilized to detect rose plant illnesses. Image acquisition, image pre-processing, picture segmentation, feature extraction, and classification are phases of disease detection. This research will look into how to save the rose plant from various diseases.