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Research Article | Open Access

A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

Xiaojun Xie1,2, Fei Xia1, Yufeng Wu3, Shouyang Liu4, Ke Yan5, Huanliang Xu1, Zhiwei Ji1,2( )
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
State Key Laboratory for Crop Genetics and Germplasm Enhancement, Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
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Abstract

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases. However, irrelevant and redundant features lead to high dimensionality. In this study, we proposed a swarm intelligence algorithm for feature selection [salp swarm algorithm for feature selection (SSAFS)] in image-based plant disease detection. SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features. To verify the effectiveness of the developed SSAFS algorithm, we conducted experimental studies using SSAFS and 5 metaheuristic algorithms. Several evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from PlantVillage. Experimental results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms, confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image classification. This computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

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Plant Phenomics
Volume 5,
2023
Article number: 0039
DOI: 10.34133/plantphenomics.0039
Cite this article:
Xie X, Xia F, Wu Y, et al. A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection. Plant Phenomics, 2023, 5: 0039. https://doi.org/10.34133/plantphenomics.0039

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Received: 14 October 2022
Accepted: 28 February 2023
Published: 11 May 2023
© 2023 Xiaojun Xie et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License (CC BY 4.0).
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