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Resource constraint crop damage classification using depth channel shuffling
Islam, Md Tanvir Swapnil, Safkat Shahrier Billal, Md. Masum Karim, Asif Shafiabady, Niusha Hassan, Md. Mehedi
Islam, Md Tanvir
Swapnil, Safkat Shahrier
Billal, Md. Masum
Karim, Asif
Shafiabady, Niusha
Hassan, Md. Mehedi
Abstract
Accurate crop damage classification is crucial for timely interventions, loss reduction, and resource optimization in agriculture. However, datasets and models for binary classification of damaged versus non-damaged crops remain scarce. To address this, we conducted an extensive study on crop damage classification using deep learning, focusing on the challenges posed by imbalanced datasets common in agriculture. We began by preprocessing the “Consultative Group for International Agricultural Research (CGIAR)” dataset to enhance data quality and balance class distributions. We created the new “Crop Damage Classification (CDC)” dataset tailored for binary classification of “Damaged” versus “Non-damaged” crops, serving as an effective training medium for deep learning models. Using the CDC dataset, we benchmarked the state-of-the-art models to evaluate their effectiveness in classifying crop damage. Leveraging the depth channel shuffling technique of ShuffleNetV2, we proposed a lightweight model “Light Crop Damage Classifier (LightCDC)”, reducing the parameters from 1.40 million to 1.13 million while achieving an accuracy of 89.44%. LightCDC outperformed existing classification and ensemble models in terms of model size, parameter count, inference time, and accuracy. Furthermore, we tested LightCDC under adverse conditions like blur, low light, and fog, validating its robustness for real-world scenarios. Thus, our contributions include a refined dataset and an efficient model tailored for crop damage classification, which is essential for timely interventions and improved crop management in resource-constrained precision agriculture. To ensure reproducibility, we released the code and dataset on GitHub.
Keywords
crop damage classification, resource constraint agriculture, precision agriculture, crop management, crop monitoring, deep learning, LightCDC
Date
2025
Type
Journal article
Journal
Engineering Applications of Artificial Intelligence
Book
Volume
144
Issue
Page Range
1-19
Article Number
Article 110117
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open
Notes
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).