Elitist TLBO for identification and verification of plant diseases

Disease identification of plants has been proved to be beneficial for agro industries, research, and environment. Due to the era of industrialization, vegetation is shrinking. Early detection of diseases by processing the image of the leaf can be rewarding and helpful in making our environment healthier and green. Data clustering is an unsubstantiated learning technology where pattern recognition is used extensively to identify diseases in plants and its main cause.The objective is divided into two components. First, the identification of the symptoms on the basis of primary cause using K- mean. Second, validating the clusters using Elitist based Teaching Learning Based Optimization (ETLBO), and finally comparing existing models with the proposed model. Implementation involves relevant data acquisition followed bypreprocessing of images. It is followed by feature extraction stage to get the best results in further classification stage. A K-mean and ETLBO algorithms are used for identification and clustering of diseases in plants. The implementation proves the suggested technique demonstrates better results on the basis of Histogram of Gradient (HoG) features. The chapter is organized as follows. In the introduction section, we have briefly explained about the existing and proposed methods. In the proposed approach section, different methods have been discussed in training and testing phases. The next section describes the algorithms used in the proposed approach followed by the experimental setup section. At the end, we have discussed analysis and comparison of experimental results. The outcome of the proposed approach provides the promising results in identification and verification of the spots disease in the plants. © 2019, Springer Nature Singapore Pte Ltd.