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Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough pp 221–232Cite as

Analysis of Multiple Component Based CNN for Similar Citrus Species Classification

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 1027))

Abstract

Automatically identifying plants from images is a trending research domain due to its importance in production and science popularization. It attempts to classify the name of a plant with a known species from a given image. The majority of existing studies on automatic plant identification focus on identifying plants with the images of leaves and a few with other plant organs like flowers, fruits, branches, etc. Classification using a single organ is not sufficiently reliable because of its interclass similarity and intraclass dissimilarity among various species. Therefore, multiple organs of a plant can be used to automatically identify plant species with the help of classification techniques by multiple input-based Convolutional Neural Network (CNN). Among several plant species, citrus represents a vast spectrum of similar plants. The current investigation was carried out to evaluate the power of using multiple organs or components on multiple input CNN to classify similar-looking citrus species. Experiments were conducted to compare classification accuracies of a single component with multiple components based CNN in classifying 10 citrus species commonly found in the northern region of India. From the analysis of results, it was observed that leaves input having top-1 accuracy of 81.1% are better in discriminating species. When input from leaves, fruits, and entire plants are combined the classification accuracy is highest with 98.2% top-5 and 91.4% top-1 accuracies followed by a combination of leaves and fruits having 96.1% top-5 and 90.7% top-1 accuracies. In this study, it has been found out that flowers are insignificant in determining citrus plant species and cause classification accuracy to dip to top-1 89.6% when combined with all other organs.

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Authors and Affiliations

  1. Department of Computer Science and IT, University of Jammu, Jammu, J&K, India

    Parul Sharma & Pawanesh Abrol

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  1. Parul Sharma
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  2. Pawanesh Abrol
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Correspondence to Parul Sharma .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India

    Vinit Kumar Gunjan

  2. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Sharma, P., Abrol, P. (2022). Analysis of Multiple Component Based CNN for Similar Citrus Species Classification. In: Gunjan, V.K., Zurada, J.M. (eds) Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough. Studies in Computational Intelligence, vol 1027. Springer, Cham. https://doi.org/10.1007/978-3-030-96634-8_20

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