Estimating spray application rates in cotton using multispectral vegetation indices obtained using an unmanned aerial vehicle

2021 ◽  
Vol 140 ◽  
pp. 105407
Author(s):  
Pedro Henrique Alves Martins ◽  
Fabio Henrique Rojo Baio ◽  
Túlio Henrique Dresch Martins ◽  
João Vitor Pereira Ferreira Fontoura ◽  
Larissa Pereira Ribeiro Teodoro ◽  
...  
2019 ◽  
Vol 62 (6) ◽  
pp. 1447-1453 ◽  
Author(s):  
Brian Richardson ◽  
Carol A. Rolando ◽  
Mark O. Kimberley ◽  
Tara M. Strand

HighlightsThe swath pattern was measured from an Agras MG-1 UAV spraying fine and extra-coarse droplet spectra.The recommended lane separation of 3.6 m did not differ for the two droplet size classes tested in this study.The applied spray deposited within the swath was higher with extra-coarse (>90%) than with fine (73%) droplets.There was potential for substantial downwind drift with fine droplets, even when flying close to the ground at low speed.Abstract. While there is increasing interest in the use of small, multi-rotor UAVs for application of agrichemicals, there is also uncertainty about their performance. Consequently, the purpose of this study was to quantify the performance of an Agras MG-1 with modified nozzle positions that, at the time of writing, was being used for commercial spraying in New Zealand. The approach was to release spray from the UAV along a single 50 m line. Spray deposits were measured using horizontal collectors placed on the ground in three 15 m transects centered on, and perpendicular to, the flight line. Airborne deposits were measured with a 10 m mast that supported spherical samplers at 1 m vertical intervals. Analysis of deposition data was undertaken to quantify factors influencing overall swath pattern variability, lane separation associated with a coefficient of variation (CV) of deposition of 30%, and spray application efficiency, which is the proportion of applied spray deposited within the swath. For two droplet size classes (extra-coarse and fine), the lane separation associated with a CV of 30% was about 3.6 m, with no significant effect of droplet size. This is a surprising result and may reflect the relatively small range of environmental conditions experienced during the field tests, including wind speed, which was relatively low for all tests. We speculate that this result may also be a consequence of the strong downwash. The swath width was positively correlated with wind speed. Spray efficiency was shown to be high (>90%) for the extra-coarse droplets but dropped significantly (73%) with the fine droplet spectrum. Combining in-swath deposition with the amount of airborne spray sampled in a 10 m vertical profile close to the edge of the swath accounted for 98.0% of the spray released with the extra-coarse spectrum but only 88.6% of the spray with the fine droplet spectrum. These results highlight that even with UAVs flying relatively close to the ground at a low forward speed, there is potential for substantial drift downwind of the swath when using smaller droplet size classes. Overall, the swath pattern was reasonably consistent across the two droplet size classes and for the narrow range of operational and meteorological conditions tested. Keywords: Aerial spraying, Pesticides, Spray application efficiency, Spray deposition, Swath pattern, UAV, Unmanned aerial vehicle.


2020 ◽  
Vol 12 (13) ◽  
pp. 2071
Author(s):  
Hwang Lee ◽  
Jinfei Wang ◽  
Brigitte Leblon

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.


2018 ◽  
Vol 110 (4) ◽  
pp. 1254-1259 ◽  
Author(s):  
Fabio Henrique Rojo Baio ◽  
Eder Eujácio Silva ◽  
Marco Antonio Vrech ◽  
Fernando Henrique Queiroz Souza ◽  
Alex Roger Zanin ◽  
...  

2020 ◽  
Vol 48 (4) ◽  
pp. 2385-2398
Author(s):  
Piyanan PIPATSITEE ◽  
Apisit EIUMNOH ◽  
Rujira TISARUM ◽  
Kanyarat TAOTA ◽  
Sumaid KONGPUGDEE ◽  
...  

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.


2019 ◽  
Vol 11 (22) ◽  
pp. 2667 ◽  
Author(s):  
Jiang ◽  
Cai ◽  
Zheng ◽  
Cheng ◽  
Tian ◽  
...  

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.


2019 ◽  
Vol 40 (1) ◽  
pp. 49 ◽  
Author(s):  
Adnane Beniaich ◽  
Marx Leandro Naves Silva ◽  
Fabio Arnaldo Pomar Avalos ◽  
Michele Duarte de Menezes ◽  
Bernardo Moreira Cândido

The permanent monitoring of vegetation cover is important to guarantee a sustainable management of agricultural activities, with a relevant role in the reduction of water erosion. This monitoring can be carried out through different indicators such as vegetation cover indices. In this study, the vegetation cover index was obtained using uncalibrated RGB images generated from a digital photographic camera on an unmanned aerial vehicle (UAV). In addition, a comparative study with 11 vegetation indices was carried out. The vegetation indices CIVE and EXG presented a better performance and the index WI presented the worst performance in the vegetation classification during the cycles of jack bean and millet, according to the overall accuracy and Kappa coefficient. Vegetation indices were effective tools in obtaining soil cover index when compared to the standard Stocking method, except for the index WI. Architecture and cycle of millet and jack bean influenced the behavior of the studied vegetation indices. Vegetation indices generated from RGB images obtained by UAV were more practical and efficient, allowing a more frequent monitoring and in a wider area during the crop cycle.


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