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Machine Learning and Convolution Neural Network Approaches to Plant Leaf Recognition

Machine Learning and Convolution Neural Network Approaches to Plant Leaf Recognition

Rajesh K. V. N., Lalitha Bhaskari D.
Copyright: © 2021 |Pages: 15
ISBN13: 9781799866596|ISBN10: 1799866599|ISBN13 Softcover: 9781799866602|EISBN13: 9781799866619
DOI: 10.4018/978-1-7998-6659-6.ch013
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MLA

K. V. N., Rajesh, and Lalitha Bhaskari D. "Machine Learning and Convolution Neural Network Approaches to Plant Leaf Recognition." Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science, edited by Mrutyunjaya Panda and Harekrishna Misra, IGI Global, 2021, pp. 234-248. https://doi.org/10.4018/978-1-7998-6659-6.ch013

APA

K. V. N., R. & D., L. B. (2021). Machine Learning and Convolution Neural Network Approaches to Plant Leaf Recognition. In M. Panda & H. Misra (Eds.), Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (pp. 234-248). IGI Global. https://doi.org/10.4018/978-1-7998-6659-6.ch013

Chicago

K. V. N., Rajesh, and Lalitha Bhaskari D. "Machine Learning and Convolution Neural Network Approaches to Plant Leaf Recognition." In Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science, edited by Mrutyunjaya Panda and Harekrishna Misra, 234-248. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6659-6.ch013

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Abstract

Plants are very important for the existence of human life. The total number of plant species is nearing 400 thousand as of date. With such a huge number of plant species, there is a need for intelligent systems for plant species recognition. The leaf is one of the most important and prominent parts of a plant and is available throughout the year. Leaf plays a major role in the identification of plants. Plant leaf recognition (PLR) is the process of automatically recognizing the plant species based on the image of the plant leaf. Many researchers have worked in this area of PLR using image processing, feature extraction, machine learning, and convolution neural network techniques. As a part of this chapter, the authors review several such latest methods of PLR and present the work done by various authors in the past five years in this area. The authors propose a generalized architecture for PLR based on this study and describe the major steps in PLR in detail. The authors then present a brief summary of the work that they are doing in this area of PLR for Ayurvedic plants.

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