Resource constraint crop damage classification using depth channel shuffling

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.