Classification Model of Grassland Desertification Based on Deep Learning

Huilin Jiang, Rigeng Wu, Yongan Zhang, Meian Li (), Hao Lian, Yikun Fan, Wenqian Yang and Peng Zhou
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Huilin Jiang: Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot 010000, China
Rigeng Wu: Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot 010000, China
Yongan Zhang: Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot 010000, China
Meian Li: Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot 010000, China
Hao Lian: Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot 010000, China
Yikun Fan: Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot 010000, China
Wenqian Yang: Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot 010000, China
Peng Zhou: Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot 010000, China

Sustainability, 2024, vol. 16, issue 19, 1-16

Abstract: Grasslands are one of the most important ecosystems on earth, and the impact of grassland desertification on the earth’s environment and ecosystem cannot be ignored. Accurately distinguishing grassland desertification types has important application value. The appropriate grazing strategies can be implemented based on these distinctions. Grassland conservation measures can be tailored accordingly. This contributes to further protecting and restoring grassland vegetation. This project takes color images labeled with the desertification types of grasslands as the research object, uses the currently popular deep learning model as the classification tool, and then establishes a color image-based grassland desertification classification model based on the feature extraction network, based on the Vision Transformer model, by comparing the various deep learning image classification models. The experimental results show that, despite the complex structure and large number of parameters of the grassland desertification classification model obtained in this project, the test accuracy rate reaches 88.72% and the training loss is only 0.0319. Compared with the popular classification models such as VGG16, ResNet50, ResNet101, DenseNet101, DenseNet169, and DenseNet201, and so on, the Vision Transformer demonstrates clear advantages in classification accuracy, fitting ability, and generalization capacity. By integrating with deep learning technology, the model can be applied to grassland management and ecological restoration. Mobile devices can be used to conveniently capture image data, and information can be processed quickly. This provides efficient tools for grazing managers, environmental scientists, and conservation organizations. These tools assist in quickly assessing the extent of grassland desertification, optimizing grassland management and conservation decisions. Furthermore, strong technical support is offered for the ecological restoration and sustainable management of desertification grasslands.

Keywords: deep learning; color image; grassland desertification; feature extraction; image classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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