Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle
Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/1/145 |
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| author | Enze Song Guangcheng Shao Xueying Zhu Wei Zhang Yan Dai Jia Lu |
| author_facet | Enze Song Guangcheng Shao Xueying Zhu Wei Zhang Yan Dai Jia Lu |
| author_sort | Enze Song |
| collection | DOAJ |
| description | Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments. The study included two treatments: I—water availability factor (i.e., three drought objects, optimal, and excess water); and II—two levels of deep percolation and five nitrogen fertilization doses. The introduced model is extreme learning machine (ELM), back propagation neural network (BPNN), and particle swarm optimization-ELM (PSO-ELM), respectively. The results showed that: (1) Proper water level regulation (3~5 cm) significantly increased the accumulation of spike biomass, which was about 6% higher compared to that under flooded conditions. (2) For plant height inversion, the ELM model was optimal with a mean coefficient of determination of 0.78, a mean root mean square error of 0.26 cm, and a mean performance deviation rate of 2.08. For biomass inversion, the PSO-ELM model was optimal with a mean coefficient of determination of 0.88, a mean root mean square error of 3.8 g, and a mean performance deviation rate of 3.29. This study provided the possible opportunity for large-scale estimations of rice yield under environmental disturbances. |
| first_indexed | 2024-03-08T11:08:15Z |
| format | Article |
| id | doaj.art-3b59adb39adc4b22a6634e6823c2e6a5 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-08T11:08:15Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-3b59adb39adc4b22a6634e6823c2e6a52024-01-26T14:25:58ZengMDPI AGAgronomy2073-43952024-01-0114114510.3390/agronomy14010145Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial VehicleEnze Song0Guangcheng Shao1Xueying Zhu2Wei Zhang3Yan Dai4Jia Lu5College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, ChinaCollege of Agricultural Science and Engineering, Hohai University, Nanjing 210098, ChinaCollege of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710049, ChinaCollege of Agricultural Science and Engineering, Hohai University, Nanjing 210098, ChinaCollege of Agricultural Science and Engineering, Hohai University, Nanjing 210098, ChinaCollege of Agricultural Science and Engineering, Hohai University, Nanjing 210098, ChinaPlant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments. The study included two treatments: I—water availability factor (i.e., three drought objects, optimal, and excess water); and II—two levels of deep percolation and five nitrogen fertilization doses. The introduced model is extreme learning machine (ELM), back propagation neural network (BPNN), and particle swarm optimization-ELM (PSO-ELM), respectively. The results showed that: (1) Proper water level regulation (3~5 cm) significantly increased the accumulation of spike biomass, which was about 6% higher compared to that under flooded conditions. (2) For plant height inversion, the ELM model was optimal with a mean coefficient of determination of 0.78, a mean root mean square error of 0.26 cm, and a mean performance deviation rate of 2.08. For biomass inversion, the PSO-ELM model was optimal with a mean coefficient of determination of 0.88, a mean root mean square error of 3.8 g, and a mean performance deviation rate of 3.29. This study provided the possible opportunity for large-scale estimations of rice yield under environmental disturbances.https://www.mdpi.com/2073-4395/14/1/145UAVmultispectral remote sensingriceELMPSO |
| spellingShingle | Enze Song Guangcheng Shao Xueying Zhu Wei Zhang Yan Dai Jia Lu Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle Agronomy UAV multispectral remote sensing rice ELM PSO |
| title | Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle |
| title_full | Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle |
| title_fullStr | Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle |
| title_full_unstemmed | Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle |
| title_short | Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle |
| title_sort | estimation of plant height and biomass of rice using unmanned aerial vehicle |
| topic | UAV multispectral remote sensing rice ELM PSO |
| url | https://www.mdpi.com/2073-4395/14/1/145 |
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