Cotton Yield Estimation from UAV-Based Plant Height

2019 ◽  
Vol 62 (2) ◽  
pp. 393-404 ◽  
Author(s):  
Aijing Feng ◽  
Meina Zhang ◽  
Kenneth A. Sudduth ◽  
Earl D. Vories ◽  
Jianfeng Zhou
Keyword(s):  
Remote Sensing ◽  
Plant Height ◽  
Crop Yield ◽  
Low Cost ◽  
Reference Points ◽  
Cotton Field ◽  
Accurate Estimation ◽  
Cotton Yield ◽  
Yield Estimation ◽  
Sensing System

Abstract. Accurate estimation of crop yield before harvest, especially in early growth stages, is important for farmers and researchers to optimize field management and evaluate crop performance. However, existing in-field methods for estimating crop yield are not efficient. The goal of this research was to evaluate the performance of a UAV-based remote sensing system with a low-cost RGB camera to estimate cotton yield based on plant height. The UAV system acquired images at 50 m above ground level over a cotton field at the first flower growth stage. Waypoints and flight speed were selected to allow >70% image overlap in both forward and side directions. Images were processed to develop a geo-referenced orthomosaic image and a digital elevation model (DEM) of the field that was used to extract plant height by calculating the difference in elevation between the crop canopy and bare soil surface. Twelve ground reference points with known height were deployed in the field to validate the UAV-based height measurement. Geo-referenced yield data were aligned to the plant height map based on GPS and image features. Correlation analysis between yield and plant height was conducted row-by-row with and without row registration. Pearson correlation coefficients between yield and plant height with row registration for all individual rows were in the range of 0.66 to 0.96 and were higher than those without row registration (0.54 to 0.95). A linear regression model using plant height was able to estimate yield with root mean square error of 550 kg ha-1 and mean absolute error of 420 kg ha-1. Locations with low yield were analyzed to identify the potential reasons, and it was found that water stress and coarse soil texture, as indicated by low soil apparent electricity conductivity (ECa), might contribute to the low yield. The findings indicate that the UAV-based remote sensing system equipped with a low-cost digital camera was potentially able to monitor plant growth status and estimate cotton yield with acceptable errors. Keywords: Cotton, Geo-registration, Plant height, UAV-based remote sensing, Yield estimation.

scholarly journals Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1231 ◽  
Author(s):  
Huilin Tao ◽  
Haikuan Feng ◽  
Liangji Xu ◽  
Mengke Miao ◽  
Guijun Yang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Plant Height ◽  
Crop Yield ◽  
Remote Sensing Data ◽  
Hyperspectral Images ◽  
Accurate Estimation ◽  
Least Squares Regression ◽  
Yield Estimation ◽  
Regression Techniques ◽  
Artificial Neural Network Ann

Crop yield is related to national food security and economic performance, and it is therefore important to estimate this parameter quickly and accurately. In this work, we estimate the yield of winter wheat using the spectral indices (SIs), ground-measured plant height (H), and the plant height extracted from UAV-based hyperspectral images (HCSM) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random Forest (RF). The SIs, H, and HCSM were used as input values, and then the PLSR, ANN, and RF were trained using regression techniques. The three different regression techniques were used for modeling and verification to test the stability of the yield estimation. The results showed that: (1) HCSM is strongly correlated with H (R2 = 0.97); (2) of the regression techniques, the best yield prediction was obtained using PLSR, followed closely by ANN, while RF had the worst prediction performance; and (3) the best prediction results were obtained using PLSR and training using a combination of the SIs and HCSM as inputs (R2 = 0.77, RMSE = 648.90 kg/ha, NRMSE = 10.63%). Therefore, it can be concluded that PLSR allows the accurate estimation of crop yield from hyperspectral remote sensing data, and the combination of the SIs and HCSM allows the most accurate yield estimation. The results of this study indicate that the crop plant height extracted from UAV-based hyperspectral measurements can improve yield estimation, and that the comparative analysis of PLSR, ANN, and RF regression techniques can provide a reference for agricultural management.

scholarly journals Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3787 ◽  
Author(s):  
Bing Yu ◽  
Songhao Shang
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Irrigation District ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Distribution Maps

Crop yield estimation is important for formulating informed regional and national food trade policies. The introduction of remote sensing in agricultural monitoring makes accurate estimation of regional crop yields possible. However, remote sensing images and crop distribution maps with coarse spatial resolution usually cause inaccuracy in yield estimation due to the existence of mixed pixels. This study aimed to estimate the annual yields of maize and sunflower in Hetao Irrigation District in North China using 30 m spatial resolution HJ-1A/1B CCD images and high accuracy multi-year crop distribution maps. The Normalized Difference Vegetation Index (NDVI) time series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to calculate daily NDVI and phenological characteristics. Eight random forest (RF) models using different predictors were developed for maize and sunflower yield estimation, respectively, where predictors of each model were a combination of NDVI series and/or phenological characteristics. We calibrated all RF models with measured crop yields at sampling points in two years (2014 and 2015), and validated the RF models with statistical yields of four counties in six years. Results showed that the optimal model for maize yield estimation was the model using NDVI series from the 120th to the 210th day in a year with 10 days’ interval as predictors, while that for sunflower was the model using the combination of three NDVI characteristics, three phenological characteristics, and two curve parameters as predictors. The selected RF models could estimate multi-year regional crop yields accurately, with the average values of root-mean-square error and the relative error of 0.75 t/ha and 6.1% for maize, and 0.40 t/ha and 10.1% for sunflower, respectively. Moreover, the yields of maize and sunflower can be estimated fairly well with NDVI series 50 days before crop harvest, which implicated the possibility of crop yield forecast before harvest.

scholarly journals A low cost remote sensing system using PC and stereo equipment

2011 ◽  
Vol 79 (12) ◽  
pp. 1240-1245 ◽  
Author(s):  
Joel F. Campbell ◽  
Michael A. Flood ◽  
Narasimha S. Prasad ◽  
Wade D. Hodson
Keyword(s):  
Remote Sensing ◽  
Low Cost ◽  
Sensing System

scholarly journals Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2439
Author(s):  
Haixiao Ge ◽  
Fei Ma ◽  
Zhenwang Li ◽  
Changwen Du
Keyword(s):  
Grain Yield ◽  
Low Cost ◽  
Vegetation Indices ◽  
Model Performance ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Rice Breeding ◽  
Yield Estimation ◽  
Phenological Data ◽  
Significant Difference

The accurate estimation of grain yield in rice breeding is crucial for breeders to screen and select qualified cultivars. In this study, a low-cost unmanned aerial vehicle (UAV) platform mounted with an RGB camera was carried out to capture high-spatial resolution images of rice canopy in rice breeding. The random forest (RF) regression techniques were used to establish yield models by using (1) only color vegetation indices (VIs), (2) only phenological data, and (3) fusion of VIs and phenological data as inputs, respectively. Then, the performances of RF models were compared with the manual observation and CERES-Rice model. The results indicated that the RF model using VIs only performed poorly for estimating yield; the optimized RF model that combined the use of phenological data and color VIs performed much better, which demonstrated that the phenological data significantly improved the model performance. Furthermore, the yield estimation accuracy of 21 rice cultivars that were continuously planted over three years in the optimal RF model had no significant difference (p > 0.05) with that of the CERES-Rice model. These findings demonstrate that the RF model, by combining phenological data and color Vis, is a potential and cost-effective way to estimate yield in rice breeding.

scholarly journals Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques

Agriculture ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 54 ◽  
Author(s):  
Mohamad Awad
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Optimization Model ◽  
Crop Yields ◽  
Potato Crop ◽  
Optimization Techniques ◽  
Yield Estimation ◽  
Climate Conditions ◽  
New Model

Many crop yield estimation techniques are being used, however the most effective one is based on using geospatial data and technologies such as remote sensing. However, the remote sensing data which are needed to estimate crop yield are insufficient most of the time due to many problems such as climate conditions (% of clouds), and low temporal resolution. There have been many attempts to solve the lack of data problem using very high temporal and very low spatial resolution images such as Modis. Although this type of image can compensate for the lack of data due to climate problems, they are only suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution remote sensing images due to climate conditions, a new optimization model was created. Crop yield estimation is improved and its precision is increased based on the new model that includes the use of the energy balance equation. To verify the results of the crop yield estimation based on the new model, information from local farmers about their potato crop yields for the same year were collected. The comparison between the estimated crop yields and the actual production in different fields proves the efficiency of the new optimization model.

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