Enhancing Crop Infection Categorization: Introducing a Novel MobilenetV1 based Oppositional Crayfish Algorithm

DOI

10.2991/978-94-6463-738-0_63How to use a DOI?

Keywords

Crop Disease; Mobilenetv1; Oppositional Strategy; Crayfish; Yield; Mobilenetv1; Oppositional Crayfish Algorithm; Crop Disease Detection; Exploration; Exploitation

Abstract

Crop infection categorization is crucial for detecting disease causes, understanding disease development patterns, and determining the extent of hazards to crops. Categorizing infections by pathogen type provides valuable insights into disease spread and aids in suggesting effective control measures. Automated crop leaf infection categorization and detection are pivotal for conserving unique species and minimizing economic losses. This work proposes a novel MobilenetV1-based Oppositional Crayfish (MV1-OCF) algorithm to address the challenges posed by genetic and phenotypic diversity in crop disease classification. Crop phenotypes exhibit significant variations, necessitating precise classification methods. The MV1-OCF algorithm leverages both the mobility of crayfish behavior and the innovative oppositional learning strategy. By incorporating forward and backward movement during the search process, the algorithm enhances exploration and exploitation capabilities, leading to more accurate and effective disease classification. Additionally, this approach considers variability in environmental conditions, such as leaf and lighting conditions, ensuring robustness in plant disease classification. By leveraging disease plant images, this work facilitates the initial detection and management of crop infections, ultimately enhancing crop yield and food production. Evaluation using PlantifyDr Dataset, Agriculture crop images, Rice Leaf Diseases datasets, and comparison with existing techniques demonstrate superior performance of the proposed method. The evaluation results reveal that the proposed technique surpasses existing methods, achieving exceptional metrics including 98.6% accuracy, 99.4% F1-score, 98.3% precision, 97.4% recall, and 99.1% specificity.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Smitha Padshetty
AU  - Ambika
PY  - 2025
DA  - 2025/06/22
TI  - Enhancing Crop Infection Categorization: Introducing a Novel MobilenetV1 based Oppositional Crayfish Algorithm
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 791
EP  - 816
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_63
DO  - 10.2991/978-94-6463-738-0_63
ID  - Padshetty2025
ER  -