Radial basis function neural network and salp swarm algorithm for paddy leaf diseases classification in Thanjavur, Tamilnadu geographical region.

Farming is an essential sustenance for the progressive population. The development of our country depends on the farmers. Plants endure by many diseases due to environmental factors. So, the farmers need to detect plant diseases at an early stage for appreciable yield. In the beginning, the observing and examining plant disease are examined physically by the expertise in the farming field, which requires a considerable measure of work/ and requires over the top handling time. Now, machine learning concepts eliminate conventional protruding and time-consuming techniques. This paper focuses on a novel method for detecting and identifying paddy leaf diseases at the early stages in Thanjavur region using radial basis function neural network (RBFNN) classifier. Further, it is optimized with salp swarm algorithm (SSA) technique. The proposed method utilizes the data from the TNAU agritech portal, IRRI knowledge bank, UCI machine learning repository databases, which have healthy and diseased images. This work illustrates four categories (Bacterial Blast, Bacterial Blight, Leaf Tungro and Brown Spot) of infected paddy images along with the normal set of images. Initially the preprocessing is performed for the acquired images then K-means segmentation algorithm segregates the image. Gray level co-occurrence matrix extracts the Texture features from the segmented image and the RBFNN classifier performs the disease classification and improves the detection accuracy by optimizing the data using SSA. The investigational results of the proposed methodology exhibit the performance in terms of accuracy of disease detection is 98.47%. However, radial basis function neural network (RBFNN) achieves the diseases detection accuracy of 97.85% and support-vector machine (SVM) classifier achieves a disease detection accuracy of 97.07%. This paper proposes a method of paddy leaf disease recognition and classification using RBFNN and salp swarm algorithm. It also suggests and identifies an image analysis by framing a set of conditions for disease affected plants. The results show that the most satisfactory outcome can be gained to verify the yield of proposed methods with least effort. [ABSTRACT FROM AUTHOR]