Abstract

The grassland ecosystem plays a crucial role in providing ample forage resources for grassland animal husbandry, ensuring its development. Identifying grassland forage is essential for understanding forage resources and cultivating high-quality forage. To address the low accuracy of forage image identification and the issue of some features being ignored during image preprocessing, we have proposed a novel forage identification model, Dual-attention Auto-Encoding Dense Convolution Network (DAEDN), which has not been applied to forage identification before. DAEDN simultaneously calculates the weighted features of both channels and spaces, and utilizes Auto-Encodings to better capture the data, thereby enhancing the feature extraction capability and identification classification performance for grassland forage data. Additionally, it enhances the analysis of edge, texture, and other detailed features by leveraging the feature reuse and direct connection of each layer in the dense convolution structure. We evaluated the model performance through six evaluation parameters including overall accuracy (OA) and average accuracy (AA) and verified the effectiveness of the model by comparing it with popular convolutional neural network models. Experimental results show that the identification accuracy of DAEDN is 98.31%. Experiments proved that DAEDN enhanced ability to extract forage features, improved identification accuracy, and offered a new approach for the identification research of forage hyperspectral images.

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