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Wang Z, Ding J, Tan J, Liu J, Zhang T, Cai W, Meng S. UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF. Front Plant Sci 2024; 15:1358965. [PMID: 38439983 PMCID: PMC10909836 DOI: 10.3389/fpls.2024.1358965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/06/2024] [Indexed: 03/06/2024]
Abstract
Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.
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Affiliation(s)
- Zeyuan Wang
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, China
| | - Jianli Ding
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, China
| | - Jiao Tan
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, China
| | - Junhao Liu
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, China
| | - Tingting Zhang
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
| | - Weijian Cai
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, China
| | - Shanshan Meng
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, China
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Yu F, Bai J, Jin Z, Zhang H, Yang J, Xu T. Estimating the rice nitrogen nutrition index based on hyperspectral transform technology. Front Plant Sci 2023; 14:1118098. [PMID: 37035061 PMCID: PMC10073549 DOI: 10.3389/fpls.2023.1118098] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVE The rapid diagnosis of rice nitrogen nutrition is of great significance to rice field management and precision fertilization. The nitrogen nutrition index (NNI) based on the standard nitrogen concentration curve is a common parameter for the quantitative diagnosis of rice nitrogen nutrition. However, the current NNI estimation methods based on hyperspectral techniques mainly focus on finding a better estimation model while ignoring the relationship between the critical nitrogen concentration curve and rice hyperspectral reflectance. METHODS This study obtained canopy spectral data using unmanned aerial vehicle (UAV) hyperspectral remote sensing and determined the rice critical nitrogen concentration curve and NNI. Taking the spectrum at critical nitrogen concentration as the standard spectrum, the original spectral reflectance and logarithmic spectral reflectance data were transformed by the difference method, and the features of the spectral data were extracted by a Autoencoder. Finally, the NNI inversion models of rice based on Extreme Learning Machine (ELM) and Bald Eagle Search-Extreme Learning Machine (BES-ELM) were constructed by taking the feature bands of four spectral extractions as input variables. RESULTS 1) from the feature extraction results of the self-encoder, simple logarithmic or difference transformation had little effect on NNI estimation, and logarithmic difference transformation effectively improved the NNI estimation results; 2) the estimation model based on the logarithmic difference spectrum and BES-ELM had the highest estimation accuracy, and the coefficient of determination (R2) values of the training set and verification set were 0.839 and 0.837, and the root mean square error (RMSE) values were 0.075 and 0.073, respectively; 3) according to the NNI, the samples were divided into a nitrogen-rich group (NNI ≥ 1) and nitrogen-deficient group (NNI < 1). CONCLUSION The logarithmic difference transformation of the spectrum can effectively improve the estimation accuracy of the NNI estimation model, providing a new approach for improving NNI estimation methods based on hyperspectral technology.
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Hu J, Zhang B, Peng D, Yu R, Liu Y, Xiao C, Li C, Dong T, Fang M, Ye H, Huang W, Lin B, Wang M, Cheng E, Yang S. Estimation of wheat tiller density using remote sensing data and machine learning methods. Front Plant Sci 2022; 13:1075856. [PMID: 36618628 PMCID: PMC9810811 DOI: 10.3389/fpls.2022.1075856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
The tiller density is a key agronomic trait of winter wheat that is essential to field management and yield estimation. The traditional method of obtaining the wheat tiller density is based on manual counting, which is inefficient and error prone. In this study, we established machine learning models to estimate the wheat tiller density in the field using hyperspectral and multispectral remote sensing data. The results showed that the vegetation indices related to vegetation cover and leaf area index are more suitable for tiller density estimation. The optimal mean relative error for hyperspectral data was 5.46%, indicating that the results were more accurate than those for multispectral data, which had a mean relative error of 7.71%. The gradient boosted regression tree (GBRT) and random forest (RF) methods gave the best estimation accuracy when the number of samples was less than around 140 and greater than around 140, respectively. The results of this study support the extension of the tested methods to the large-scale monitoring of tiller density based on remote sensing data.
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Affiliation(s)
- Jinkang Hu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Bing Zhang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Dailiang Peng
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- International Research Center of Big Data for Sustainable Development Goals, Beijing, China
| | - Ruyi Yu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Yao Liu
- Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing, China
| | - Chenchao Xiao
- Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing, China
| | - Cunjun Li
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Tao Dong
- Aerospace ShuWei High Tech. Co., Ltd., Beijing, China
| | - Moren Fang
- Beijing Azup Scientific Co., Ltd., Beijing, China
| | - Huichun Ye
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- International Research Center of Big Data for Sustainable Development Goals, Beijing, China
| | - Wenjiang Huang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- International Research Center of Big Data for Sustainable Development Goals, Beijing, China
| | - Binbin Lin
- Department of Geography, Texas A&M University, TX, United States
| | - Mengmeng Wang
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, China
| | - Enhui Cheng
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Songlin Yang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
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