Accurate crop damage detection is vital for sustaining agricultural productivity and ensuring food security, particularly within the scope of precision farming. Despite significant advancements in Deep Learning (DL), the classification of crop damage under diverse real-world conditions remains challenging due to heterogeneous damage patterns, variable crop types, and environmental inconsistencies. To overcome these challenges, a hybrid DL framework (i.e., EnCropNet) is proposed for binary crop damage classification using field-level RGB images. The proposed model integrates the global semantic representation power of DenseNet121 with a lightweight convolutional stream inspired by ShuffleNetV2, enhanced via Squeeze-and-Excitation (SE) blocks for improved channel-wise attention. The model was trained and evaluated on a balanced version of the publicly available CGIAR Crop Damage Classification (CDC) dataset. To enhance generalization, extensive augmentation techniques such as random rotations, brightness variations, and zoom transformations were applied. The proposed model outperformed leading baseline models, including LightCDC, DenseNet121, EfficientNetV2S, and ShuffleNetV2, achieving a notable test accuracy of 90.00%. Additionally, visualization tools such as GradCAM and t-SNE confirm EnCropNet ability to capture discriminative features while maintaining transparency. The results suggest that EnCropNet is an effective and scalable solution for real-time crop damage assessment, particularly in low-resource agricultural environments. Its deployment could support timely decision-making and contribute to sustainable farming practices.
Shahzad et al. (Fri,) studied this question.