Research on Crop Image Classification and Recognition Based on Improved HRNet

In agricultural production, crop images are commonly used for the classification and identification of various crops. However, several challenges arise, including low image clarity, elevated noise levels, low accuracy, and poor robustness of existing classification models. To address these issues, this research proposes an innovative crop image classification model named Lap-FEHRNet, which integrates a Laplacian Pyramid Super Resolution Network (LapSRN) with a feature enhancement high-resolution network based on attention mechanisms (FEHRNet). To mitigate noise interference, this research incorporates the LapSRN network, which utilizes a Laplacian pyramid structure to extract multi-level feature details from low-resolution images through a systematic layer-by-layer amplification and pixel detail superposition process. This gradual reconstruction enhances the high-frequency information of the image, enabling super-resolution reconstruction of low-quality images. To obtain a broader range of comprehensive and diverse features, this research employs the FEHRNet model for both deep and shallow feature extraction. This approach results in features that encapsulate multi-scale information and integrate both deep and shallow insights. To effectively fuse these complementary features, this research introduces an attention mechanism during the feature enhancement stage. This mechanism highlights important regions within the image, assigning greater weights to salient features and resulting in a more comprehensive and effective image feature representation. Consequently, the accuracy of image classification is significantly improved. Experimental results demonstrate that the Lap-FEHRNet model achieves impressive classification accuracies of 98.8% on the crop classification dataset and 98.57% on the rice leaf disease dataset, underscoring the model’s outstanding accuracy, robustness, and generalization capability.