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Classification in BioApps pp 199–226Cite as

Machine Learning Based Plant Leaf Disease Detection and Severity Assessment Techniques: State-of-the-Art

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Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 26))

Abstract

Agriculture plays a crucial role in the economic growth of a country as it is one of the main means of subsistence. Recently, technological methods have been designed for the identification of plants and detection of their diseases in order to meet the new challenges facing farmers and their learning needs. This chapter provides an overview of various methods and techniques for feature extraction, segmentation and the classification of patterns of captured leaves in order to identify plant leaf diseases and the estimation of their severity. This chapter analyzes various automatic grading systems and parameters used in estimating the severity of different plant diseases and discusses a variety those plant diseases. The chapter also discusses the use of a radial basis function (RBF) kernel-based support vector machine (SVM) learning algorithm for the detection of these diseases, which include rust; tikka; powdery and downy mildew; late blight and early blight in groundnut, apple, potato and tomato plants. An analysis is made of the factors that stress plants; for example, water, pests and soil in green house plants. Leaf blast, brown spot and sheath rot detected in rice plants are discussed using a cluster validation algorithm. The chapter also deals with the assessment of severity of foliar diseases found in soybean plants (using segmentation), rice blast disease (using an SVM) and single disease severity level of plasmopara viticola in grape leaves (using a k-means clustering algorithm). The existing gaps in the technology for precision agriculture are discussed, and the requirements and demands to be met by agriculturists are presented. The whole impetus of this chapter is to motivate researchers to focus and develop efficient machine learning and classification techniques for leaf identification and disease detection to meet the new challenges in the field of agriculture.

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Authors and Affiliations

  1. K.S. Institute of Technology, Bangalore, Karnataka, India

    Pragati Pukkela & Surekha Borra

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  1. Pragati Pukkela
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  2. Surekha Borra
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Correspondence to Pragati Pukkela .

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Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

  2. Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  3. K.S. Institute of Technology, Bangalore, Karnataka, India

    Surekha Borra

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Pukkela, P., Borra, S. (2018). Machine Learning Based Plant Leaf Disease Detection and Severity Assessment Techniques: State-of-the-Art. In: Dey, N., Ashour, A., Borra, S. (eds) Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-65981-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-65981-7_8

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  • Print ISBN: 978-3-319-65980-0

  • Online ISBN: 978-3-319-65981-7

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