Detection of Diseased Plant Leaf Using Deep Learning

https://doi.org/10.46610/JOSP.2023.v09i01.005

Abstract

The detection of illness in crops is one of the laborious and crucial duties in agricultural activities. It requires a lot of time and requires specialized labor. Detection of plant disease by use of the automatic technique is beneficial and it requires a huge amount of work, monitoring in the large farm of crops, and at an early stage itself to detect symptoms of diseases affected means where they appear on the plant leaves. A more recent, cutting-edge method for processing images that produce exact outcomes is deep learning. Leaf detection of disease and categorization employ a variety of deep learning and image processing approaches. For disease detection, image processing methods including image pre-processing, segment, feature extraction, etc. are employed along with deep learning methods like CNN, Fast RCNN, Faster RCNN, and Mask RCNN. Furthermore, deep learning technologies offer greater precision than image processing technology. Application areas for the detection of plant leaf diseases include biological research and agricultural institutes, among many others. The economy heavily depends on agricultural output. In this study, an effective method for crop disease identification by the use of machine learning and image processing techniques is proposed. This proposed approach has a 75% accuracy rate for detecting 20 distinct illnesses in 4 popular types of plants. We are using integrated techniques to improve the accuracy above 75% through this model.