scholarly journals Ramie Yield Estimation Based on UAV RGB Images

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 669
Author(s):  
Hongyu Fu ◽  
Chufeng Wang ◽  
Guoxian Cui ◽  
Wei She ◽  
Liang Zhao
Keyword(s):  
Linear Regression ◽  
Plant Height ◽  
Growth Period ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Yield Estimation ◽  
Estimation Model ◽  
Multiple Characteristic ◽  
Phenotypic Data ◽  
Uav Images

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.

scholarly journals Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

2020 ◽  
Vol 12 (3) ◽  
pp. 508 ◽  
Author(s):  
Zhaopeng Fu ◽  
Jie Jiang ◽  
Yang Gao ◽  
Brian Krienke ◽  
Meng Wang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Yield Estimation ◽  
Wheat Growth ◽  
Aerial Vehicle

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.

scholarly journals Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

2019 ◽  
Vol 11 (10) ◽  
pp. 1226 ◽  
Author(s):  
Jianqing Zhao ◽  
Xiaohu Zhang ◽  
Chenxi Gao ◽  
Xiaolei Qiu ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Similarity Index ◽  
Crop Growth ◽  
Contrast Threshold ◽  
Growth Monitoring ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Aerial Vehicle ◽  
Uav Images

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

scholarly journals Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

2020 ◽  
Vol 12 (7) ◽  
pp. 1207 ◽  
Author(s):  
Jian Zhang ◽  
Chufeng Wang ◽  
Chenghai Yang ◽  
Tianjin Xie ◽  
Zhao Jiang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Multispectral Images ◽  
Area Index ◽  
Uav Images

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

Comparison of UAV RGB Imagery and Hyperspectral Remote-sensing Data for Monitoring Winter-wheat Growth

2021 ◽  
Author(s):  
Haikuan Feng ◽  
Huilin Tao ◽  
Chunjiang Zhao ◽  
Zhenhai Li ◽  
Guijun Yang
Keyword(s):  
Remote Sensing ◽  
Spectral Index ◽  
Vegetation Index ◽  
Crop Growth ◽  
Hyperspectral Data ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Estimation Model ◽  
Area Index ◽  
Simple Ratio

Abstract Background: Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unnamed aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. Results: To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into a new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results show that (1) the RGB-imagery indices red reflectance (r), the excess-red index(EXR), the vegetation atmospherically resistant index(VARI), and the modified green-red vegetation index(MGRVI) , as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index(MSR), the simple ratio vegetation index(SR), and the normalized difference vegetation index (NDVI)are more strongly correlated with the CGI than a single growth-monitoring indicator (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stage in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.Conclusions: UAVs carrying RGB cameras and hyperspectral cameras have high inversion CGI accuracy and can judge the overall growth of wheat can provide a reference for monitoring the growth of wheat.

Role of Geospatial Technology in Crop Growth Monitoring and Yield Estimation

2021 ◽  
pp. 273-290
Author(s):  
P. K. Kingra ◽  
Raj Setia ◽  
Jatinder Kaur ◽  
R. K. Pal ◽  
Som Pal Singh
Keyword(s):  
Crop Growth ◽  
Growth Monitoring ◽  
Yield Estimation ◽  
Geospatial Technology

Research on Yield Estimate Dynamic Model of Rice in Chongqing

2013 ◽  
Vol 295-298 ◽  
pp. 2399-2403
Author(s):  
Yan Ying Chen ◽  
Yang Sheng You
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Dynamic Assessment ◽  
Growth Period ◽  
Assessment Model ◽  
Yield Estimation ◽  
Estimation Model ◽  
Yield Data ◽  
Dynamic Yield ◽  
Actual Yield

In this paper, MODIS-250m vegetation index from first 10-day of March to last 10-day of November in 2010and 2011, land use data and the yield data from 9 counties such as Jiangjin, Wanzhou, Fengdu, Liangping, Qijiang, Liangping, Fuling, Yunyang, Kaixian, Wixi in Chongqing were used. Based on GIS，RS technique and NDVI analysis results, the rice planting area was extracted. Four key growth period of rice such tillering, jointing, heading and milk were selected to establish dynamic assessment model. In nine counties, the average relative error of the remaining 8 counties was between 5.36%-17.09%, and the mean was 12.31% except Qijiang that estimation results and actual yield deviation was too large. The results show that building rice yield estimation model using normalized difference vegetation index was feasible that can realize the dynamic yield estimation of rice.

Regional Yield Estimation for Spring Maize with Multi-Temporal Remotely Sensed Data in Junchuan, China

2012 ◽  
Vol 610-613 ◽  
pp. 3601-3605
Author(s):  
Tao Su ◽  
Shao Yuan Feng ◽  
Xing Yuan Cui
Keyword(s):  
Maize Yield ◽  
Remotely Sensed ◽  
Crop Growth ◽  
Entropy Method ◽  
Yield Estimation ◽  
Estimation Model ◽  
Model Based ◽  
Combination Forecasting ◽  
Spring Maize ◽  
Multi Temporal

Establishing timely and high accurate models for crop yield estimation is of great significance for crop management and as well as decision makers. The arm of this study is to gain an approach of the method, depending on crop growth model and entropy method, to estimate spring maize yield with multi-temporal remotely sensed Landsat TM/ETM+ data at main growth and development stages of spring maize. The experiment had been conducted in Junchuan Farm of Northeast China. In this paper, the combined weights of the single-temporal estimation models were calculated by applying the entropy method (EM), and a combination forecasting (CF) model was developed. In order to improve the rationality of CF-EM and the accuracy of yield estimation, especially to follow the law of crop growth, the combination forecasting of combined weights method (CF-CM) was developed. The results showed that the yield estimation model based on CF-CM could increase the precision of the yield estimation model based on single-temporal remote images, the correlation coefficient was remarkably improved, and the value was increased by 0.09. The combined weights in the CF-CM were proposed for selecting the favorable coefficient of the subjective weight and objective weight, and that was of great importance for some key aspects: supplying usefulness information, how to raise maize yield and selecting key temporal satellite images to estimate maize yield. The CF-CM model discussed in this paper is feasible and effective to estimate spring maize yield.

scholarly journals Effect of NPK on Vegetative Growth and Yield of Desiree and Kufri Sindhuri Potato

2014 ◽  
Vol 9 ◽  
pp. 67-75
Author(s):  
Ram C. Adhikari
Keyword(s):  
Plant Height ◽  
Vegetative Growth ◽  
Sandy Loam ◽  
Growth Period ◽  
Crop Growth ◽  
Growth And Yield ◽  
Maturity Stage ◽  
Fresh Weight ◽  
Yield Increase ◽  
Number Of Stems

A field experiment was carried out to assess the effect of NPK on vegetative growth and yield of potato cultivars; Kufri Sindhuri and Desiree at different nutrient levels (0:0:0, 50:50:50, 100:50:50, 100:75:50, 100:75:100, 100:100:100 and 150:100:100 N, P205 and k20 kg ha-1) in sandy loam soils at Rampur, Chitwan, Nepal during 1999/2000. The experiment was laid out in a split plot design with 4 replications. Plant height, number of stems, fresh weight of stem and leaves were recorded at 15 days interval during crop growth period and tuber yield at maturity stage. Kufri Sindhuri was taller than Desiree at all the stages of plant growth. Increasing levels of NPK increased the plant height by 15-42 percent. The levels of NPK imparted to a significant effect on fresh weight of leaves and stems at each successive   stages of crop growth. Kufri Sindhuri responded nitrogen up to 150 kg ha-1 while Desiree yielded higher at 100:100:100 kg NPK ha-1. The yield increase of potato tuber was associated with increase in the plant height, fresh weight of leaves and stems as a result of applied NPK.Nepal Agric. Res. J. Vol. 9, 2009, pp. 67-75DOI: http://dx.doi.org/10.3126/narj.v9i0.11643

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