Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices
To obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/2/167 |
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| author | Yiliang Kang Yang Wang Yanmin Fan Hongqi Wu Yue Zhang Binbin Yuan Huijun Li Shuaishuai Wang Zhilin Li |
| author_facet | Yiliang Kang Yang Wang Yanmin Fan Hongqi Wu Yue Zhang Binbin Yuan Huijun Li Shuaishuai Wang Zhilin Li |
| author_sort | Yiliang Kang |
| collection | DOAJ |
| description | To obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation indices were screened by correlation analysis, and eight textural features were extracted from five single bands in three fertility periods. Subsequently, models for estimating winter wheat yield were developed utilizing multiple linear regression (MLR), partial least squares (PLS), BP neural network (BPNN), and random forest regression (RF), respectively. (1) The results indicated a consistent correlation between the two variable types and yield across various fertility periods. This correlation consistently followed a sequence: heading period > filling period > mature stage. (2) The model’s accuracy improves significantly when incorporating both texture features and vegetation indices for estimation, surpassing the accuracy achieved through the estimation of a single variable type. (3) Among the various models considered, the partial least squares (PLS) model integrating texture features and vegetation indices exhibited the highest accuracy in estimating winter wheat yield. It achieved a coefficient of determination (R<sup>2</sup>) of 0.852, a root mean square error (RMSE) of 74.469 kg·hm<sup>−2</sup>, and a normalized root mean square error (NRMSE) of 7.41%. This study validates the significance of utilizing image texture features along with vegetation indices to enhance the accuracy of models estimating winter wheat yield. It demonstrates that UAV multispectral images can effectively establish a yield estimation model. Combining vegetation indices and texture features results in a more accurate and predictive model compared to using a single index. |
| first_indexed | 2024-03-07T22:46:12Z |
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| id | doaj.art-0569624a97df4fe2bb63b494cf0afe11 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-07T22:46:12Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-0569624a97df4fe2bb63b494cf0afe112024-02-23T15:03:28ZengMDPI AGAgriculture2077-04722024-01-0114216710.3390/agriculture14020167Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature IndicesYiliang Kang0Yang Wang1Yanmin Fan2Hongqi Wu3Yue Zhang4Binbin Yuan5Huijun Li6Shuaishuai Wang7Zhilin Li8College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Grass Industry, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaTo obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation indices were screened by correlation analysis, and eight textural features were extracted from five single bands in three fertility periods. Subsequently, models for estimating winter wheat yield were developed utilizing multiple linear regression (MLR), partial least squares (PLS), BP neural network (BPNN), and random forest regression (RF), respectively. (1) The results indicated a consistent correlation between the two variable types and yield across various fertility periods. This correlation consistently followed a sequence: heading period > filling period > mature stage. (2) The model’s accuracy improves significantly when incorporating both texture features and vegetation indices for estimation, surpassing the accuracy achieved through the estimation of a single variable type. (3) Among the various models considered, the partial least squares (PLS) model integrating texture features and vegetation indices exhibited the highest accuracy in estimating winter wheat yield. It achieved a coefficient of determination (R<sup>2</sup>) of 0.852, a root mean square error (RMSE) of 74.469 kg·hm<sup>−2</sup>, and a normalized root mean square error (NRMSE) of 7.41%. This study validates the significance of utilizing image texture features along with vegetation indices to enhance the accuracy of models estimating winter wheat yield. It demonstrates that UAV multispectral images can effectively establish a yield estimation model. Combining vegetation indices and texture features results in a more accurate and predictive model compared to using a single index.https://www.mdpi.com/2077-0472/14/2/167wheatUAV multispectral imageryyield predictioncolor indextextural features |
| spellingShingle | Yiliang Kang Yang Wang Yanmin Fan Hongqi Wu Yue Zhang Binbin Yuan Huijun Li Shuaishuai Wang Zhilin Li Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices Agriculture wheat UAV multispectral imagery yield prediction color index textural features |
| title | Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices |
| title_full | Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices |
| title_fullStr | Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices |
| title_full_unstemmed | Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices |
| title_short | Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices |
| title_sort | wheat yield estimation based on unmanned aerial vehicle multispectral images and texture feature indices |
| topic | wheat UAV multispectral imagery yield prediction color index textural features |
| url | https://www.mdpi.com/2077-0472/14/2/167 |
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