Ramie Yield Estimation Based on UAV RGB Images
| Autor: | Guoxian Cui, Liang Zhao, Wei She, Hongyu Fu, Chufeng Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Letter
010504 meteorology & atmospheric sciences Correlation coefficient Mean squared error yield estimation 0211 other engineering and technologies 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Boehmeria Analytical Chemistry Ramie Linear regression lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics Remote sensing deep learning Vegetation Stepwise regression Atomic and Molecular Physics and Optics ramie Yield (chemistry) Remote Sensing Technology RGB color model RGB images |
| Zdroj: | Sensors, Vol 21, Iss 669, p 669 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | Timely and accurate crop growth monitoring and yield estimation are important for field management. The traditional sampling method used for estimation of ramie yield is destructive. Thus, this study proposed a new method for estimating ramie yield based on field phenotypic data obtained from unmanned aerial vehicle (UAV) images. A UAV platform carrying RGB cameras was employed to collect ramie canopy images during the whole growth period. The vegetation indices (VIs), plant number, and plant height were extracted from UAV-based images, and then, these data were incorporated to establish yield estimation model. Among all of the UAV-based image data, we found that the structure features (plant number and plant height) could better reflect the ramie yield than the spectral features, and in structure features, the plant number was found to be the most useful index to monitor the yield, with a correlation coefficient of 0.6. By fusing multiple characteristic parameters, the yield estimation model based on the multiple linear regression was obviously more accurate than the stepwise linear regression model, with a determination coefficient of 0.66 and a relative root mean square error of 1.592 kg. Our study reveals that it is feasible to monitor crop growth based on UAV images and that the fusion of phenotypic data can improve the accuracy of yield estimations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|