Correlation analysis of vegetation indices based on multi-temporal satellite images and unmanned aerial vehicle images with wheat protein contents

2019 ◽  
Author(s):  
Mengmeng Du ◽  
Noboru Noguchi ◽  
Atsushi Ito ◽  
Yukinori Shibuya
Keyword(s):  
Correlation Analysis ◽  
Unmanned Aerial Vehicle ◽  
Satellite Images ◽  
Vegetation Indices ◽  
Wheat Protein ◽  
Multi Temporal ◽  
Aerial Vehicle

scholarly journals Use of Unmanned Aerial Vehicle for Multi-temporal Monitoring of Soybean Vegetation Fraction

2016 ◽  
Vol 41 (2) ◽  
pp. 126-137 ◽  
Author(s):  
Hee Sup Yun ◽  
Soo Hyun Park ◽  
Hak-Jin Kim ◽  
Wonsuk Daniel Lee ◽  
Kyung Do Lee ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Fraction ◽  
Multi Temporal ◽  
Aerial Vehicle

scholarly journals Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn

2020 ◽  
Vol 12 (13) ◽  
pp. 2071
Author(s):  
Hwang Lee ◽  
Jinfei Wang ◽  
Brigitte Leblon
Keyword(s):  
Linear Regression ◽  
Unmanned Aerial Vehicle ◽  
Random Forests ◽  
Vegetation Indices ◽  
N Fertilization ◽  
Yield Potential ◽  
Support Vector ◽  
Aerial Vehicle ◽  
Marginal Improvement ◽  
Canopy Nitrogen

The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N) supply to the crop N demand is the subject of intense research due to the environmental and economic impact of N fertilization. Excess N could seep into the water supplies around the field and cause unnecessary spending by the farmer. The drawbacks of N deficiency on crops include poor plant growth, ultimately reducing the final yield potential. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery to predict canopy nitrogen weight (g/m2) of corn fields in south-west Ontario, Canada. Simple/multiple linear regression, Random Forests, and support vector regression (SVR) were established to predict the canopy nitrogen weight from individual multispectral bands and associated vegetation indices (VI). Random Forests using the current techniques/methodologies performed the best out of all the models tested on the validation set with an R2 of 0.85 and Root Mean Square Error (RMSE) of 4.52 g/m2. Adding more spectral variables into the model provided a marginal improvement in the accuracy, while extending the overall processing time. Random Forests provided marginally better results than SVR, but the concepts and analysis are much easier to interpret on Random Forests. Both machine learning models provided a much better accuracy than linear regression. The best model was then applied to the UAV images acquired at different dates for producing maps that show the spatial variation of canopy nitrogen weight within each field at that date.

scholarly journals Above-ground vegetation indices and yield attributes of rice crop using unmanned aerial vehicle combined with ground truth measurements

2020 ◽  
Vol 48 (4) ◽  
pp. 2385-2398
Author(s):  
Piyanan PIPATSITEE ◽  
Apisit EIUMNOH ◽  
Rujira TISARUM ◽  
Kanyarat TAOTA ◽  
Sumaid KONGPUGDEE ◽  
...  
Keyword(s):  
Plant Height ◽  
Unmanned Aerial Vehicle ◽  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Grain Weight ◽  
Yield Traits ◽  
Aerial Vehicle ◽  
Ground Truthing ◽  
Non Destructive

Rice is an important economic and staple crop in several developing countries. Indica rice cultivars, ‘KDML105’ and ‘RD6’ are clear favourites, popular throughout world for their cooking quality, aroma, flavour, long grain, and soft texture, thus consequently dominate major plantation area in Northeastern region of Thailand. The objective of present study was to validate UAV (unmanned aerial vehicle)-derived information of rice crop traits with ground truthing non-destructive measurements in these rice varieties throughout whole life span under field environment. Plant height of cv. ‘KDML105’ was more than cv. ‘RD6’ for each respective stage. Whereas, number of tillers per clump in ‘KDML105’ exhibited stability at each developmental stage, which was in contrast to ‘RD6’ (increased continuously). Moreover, 1,000 grain weight, total grain weight and aboveground biomass were higher in ‘KDML105’ than in ‘RD6’ by 1.20, 1.82 and 3.82 folds. Four vegetative indices, ExG, EVI2, NDVI and NDRE derived from UAV platform proved out to be excellent parameters to compare KDML105 and RD6, especially in the late vegetative and reproductive developmental stages. Positive relationships between NDVI and NDRE, NDRE and total yield traits, as well as NDVI and aboveground biomass were demonstrated. In contrast, total chlorophyll pigment in cv. ‘RD6’ was higher than in cv. ‘KDML105’ leading to negative correlation with NDVI. ‘KDML105’ reflected rapid adaptation to Northeastern environments, leading to maintenance of plant height and yield components. Vegetation indices derived from UAV platform and ground truth non-destructive data exhibited high correlation. ‘KDML105’ was rapidly adapted to NE environments when compared with ‘RD6’, leading to maintenance of physiological parameters (detecting by UAV), the overall growth performances and yield traits (measuring by ground truth method). This study advocates harnessing and adopting the approach of UAV platform along with ground truthing non-destructive measurements of assessing a species/cultivars performance at broad land-use scale.

scholarly journals Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat

2019 ◽  
Vol 11 (22) ◽  
pp. 2667 ◽  
Author(s):  
Jiang ◽  
Cai ◽  
Zheng ◽  
Cheng ◽  
Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Digital Cameras ◽  
Status Monitoring ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
N Status

Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.

scholarly journals Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping

Agriculture ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 65 ◽  
Author(s):  
Robin Mink ◽  
Avishek Dutta ◽  
Gerassimos Peteinatos ◽  
Markus Sökefeld ◽  
Johannes Engels ◽  
...  
Keyword(s):  
Sugar Beet ◽  
Weed Control ◽  
Unmanned Aerial Vehicle ◽  
Cirsium Arvense ◽  
Rumex Crispus ◽  
Site Specific ◽  
Multi Temporal ◽  
Aerial Vehicle
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