Acta Optica Sinica, Volume. 45, Issue 6, 0628001(2025)
Machine Learning for Hyperspectral Characteristic Band Extraction in Soil Nutrient Analysis
Fig. 3. Flowchart of RF-DE-AHP method for extracting soil nutrient characteristic bands
Fig. 5. Mutual information between soil available potassium and spectral data by RF algorithm
Fig. 6. Spectral band weight distribution of soil available potassium by using differential evolution algorithm
Fig. 7. Weight sorting for soil available potassium by the analytic hierarchy process
Fig. 8. RF-DE-AHP method prediction results. (a) Training set; (b) validation set
Fig. 9. Mathematical transformation method prediction results. (a) Training set; (b) validation set
Fig. 11. Evaluation results of the available potassium prediction models. (a) Training set; (b) validation set
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Kai Liu, Yufeng Wang, Zhiqing Peng, Jingjing Liu, Yuehui Song, Huige Di, Dengxin Hua. Machine Learning for Hyperspectral Characteristic Band Extraction in Soil Nutrient Analysis[J]. Acta Optica Sinica, 2025, 45(6): 0628001
Category: Remote Sensing and Sensors
Received: May. 24, 2024
Accepted: Jul. 11, 2024
Published Online: Mar. 21, 2025
The Author Email: Wang Yufeng (wangyufeng@xaut.edu.cn)