Fuzzy C-Means (FCM) Clustering with Probabilistic Neural Network (PNN) Model for Detection and Classification of Rice Plant Diseases in Internet of Things-Cloud Centric Precision Agriculture

Presently, the field of Internet of Things (loT) has been employed in diverse applications like Smart Grid, Surveillance, Smart homes, and so on. Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop health. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. This paper introduces an effective rice plant disease identification and classification model to identify the type of disease from infected rice plants. The proposed method aims to detect three rice plant diseases such as Bacterial leaf blight, Brown spot, and Leaf smut. The proposed method involves a set of different processes namely image acquisition, preprocessing, segmentation, feature extraction and classification. At the earlier stage, IoT devices will be used to capture the image and stores it with a cloud server, which executes the classification process. In the cloud, the rice plant images under preprocessing to improvise the quality of the image. Then, fuzzy c-means (FCM) clustering method is utilized for the segmentation of disease portion from a leaf image. Afterwards, feature extraction takes place under three kinds namely color, shape, and texture. Finally, probabilistic neural network (PNN) is applied for multi-class classification. A detailed experimental analysis ensured the effective classification performance of the proposed method under all the test images applied.