Title: Recognition of crop leaf diseases based on multi-feature fusion and evolutionary algorithm optimisation

Authors: Lixia Zhang; Kangshun Li; Yu Qi

Addresses: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China ' College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China; School of Computer and Informatics, Dongguan City College, Dongguan, China ' College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

Abstract: Crop leaf disease identification refers to automatically recognise crop leaf pictures suffering from disease, to determine the type of diseases, which is important for agricultural production. Much progress has been made in this field, but there are still many challenges. For example, there are not enough ideal schemes for either disease spot area segmentation or feature representation and matching. In order to meet these challenges, a new crop leaf disease recognition method was proposed in this paper. First, disease spot segmentation method combined ultra-green feature and threshold segmentation was presented. Then, feature representation scheme with multiple features was proposed, which combined colour, texture, and shape features. Finally, evolutionary algorithm was used to optimise similarity function for feature matching. Experimental results show that the scheme proposed in this paper can effectively improve recognition accuracy and has a certain practical value.

Keywords: crop leaf disease recognition; evolutionary algorithm; disease spot area segmentation; feature representation; feature matching.

DOI: 10.1504/IJBIC.2023.131826

International Journal of Bio-Inspired Computation, 2023 Vol.21 No.3, pp.163 - 173

Received: 27 Oct 2021
Accepted: 20 Mar 2022
Published online: 04 Jul 2023 *

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