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 rewardin
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Abstract 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 by preprocessing 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. Keywords Pattern recognition · Teaching Learning Based Optimization (TLBO) · Histogram of Gradients (HoG) · Independent component analysis T. Jena (B) · T. M. Rajesh · M. Patil Dayananda Sagar University, Bangalore 560068, India e-mail: [email protected] T. M. Rajesh e-mail: [email protected] M. Patil e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 A. J. Kulkarni et al. (eds.), Socio-cultural Inspired Metaheuristics, Studies in Computational Intelligence 828, https://doi.org/10.1007/978-981-13-6569-0_3
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1 Introduction Human race depends on plants directly or indirectly for its survival. Plants give us food, clothes, medicine, furniture, and much more things. Healthy plants mean better quality of life for human being. Diseases in plants can decrease the production, increase the cost, and might range to the overall economic adversity of produce if not alleviated suitably at initial phases [1, 2]. The crops need planned nursing to distinguish the early indications in demand to avoid the feast of any plant infection, with low cost and better yield in manufacture. Employing trained agriculturists might not be reasonable particularly in isolated topographical regions. Computer vision can propose a substitute answer in plant nursing and s
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