Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service

Maresma, Ángel; Ariza, Mar; Martínez, E.; Lloveras, J.; Martínez‐Casasnovas, José A.

doi:10.3390/rs8120973

Cited by 157 publications

(114 citation statements)

References 38 publications

Supporting

Mentioning

101

Contrasting

Unclassified

Order By: Relevance

“…WDRVI and EVI were respectively saturated at 45% and 58% of the maximum yield, and, together with the PltH (56%), were the least accurate at differentiating among the highest grain yields. These results partially disagree with those presented by Maresma et al [35], who in a one year-site study found the WDRVI to be the best grain predictor using a high-resolution UAV service. Nevertheless, although in the present study the plateau was not reached for high relative grain yield, there was a clear tendency of increasing WDRVI values when increasing the yield (Figure 5f).…”

Section: Correlation Between Vegetation Indices Spad and Plant Heighcontrasting

confidence: 99%

“…However, the maize N extractions were probably not completely covered by the determined GYON r and GYON a in the linear-plateau model. In similar environments, Maresma et al [35] and Yagüe and Quílez [70] reported respectively a GYON r of 239.8 and 300 kg N ha −1 , where the N extraction of maize is around 250-300 kg N ha −1 [8][9][10]. These N rates can be considered low compared to the extractions, but if N is provided when needed, maize could uptake the N and translates it into yield.…”

Section: Grain Yield and Economic Return Responses To N Rates And Avamentioning

confidence: 99%

“…Hence, maize ground cover is vital if the pixel resolution does not allow removal of soil pixels [30]. However, image acquisition after V8 stage seems to be consistently useful to determine maize N status and to predict yield [23,[31][32][33][34][35], increasing progressively its accuracy up to VT stage (tasseling). In areas where N can be applied via irrigation systems, the image acquisition at V12 stage is generally preferred to other alternatives.…”

Section: Introductionmentioning

confidence: 99%

“…Satellite images normally have lower spatial and temporal resolution and, in some areas, they can be disturbed by cloud cover and/or sprinkler irrigation during the image acquisition [40]. Unmanned aerial vehicles (UAV) have tremendous potential for high-resolution requirements for detailed site-specific weed control treatments in early post-emergence [41] or N recommendation [35]. However, such high-resolution is probably not necessary for determining maize N status at V12 (when the canopy covers the ground entirely), and airborne images could be useful for practical farming implementation because of their high potential to cover large cropping areas.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments

2018

Self Cite

View full text Add to dashboard Cite

Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year study, multispectral VIs (green-and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured at V12-VT stage of maize development, were used to distinguish among the N status of maize, to predict grain yield and economic return in high yielding environments. Moreover, linear plateau-models were performed with VIs, SPAD and PltH measurements to determine the amount of N needed to achieve maximum maize grain yields and economic return. The available N in the topsoil (0-30 cm) was measured, and its relationship with VIs, maize yield and maize N requirements was analyzed. Green-based VIs were the most accurate indices to predict grain yield and to estimate the grain yield optimum N rate (GYON r ) (216.8 kg N ha −1 ), but underestimated the grain yield optimum N available (GYON a ) (248.6 kg N ha −1 ). Red-based VIs slightly overestimated the GYON r and GYON a , while SPAD highly underestimated both of them. The determination of the available N did not improve the accuracy of the VIs to determine the grain yield. The green chlorophyll index (GCI) distinguished maize that would yield less than 84% of the maximum yield, showing a high potential to detect and correct maize N deficiencies at V12 stage. The economic optimum nitrogen rate (EON r ) and economic optimum nitrogen available (EON a ) were determined below the GYON r and the GYON a , demonstrating that maximum grain yield strategies in maize are not normally the most profitable for farmers. Further research is needed to fine-tune the response of maize to N applications when deficiencies are detected at V12 stage, but airborne imagery could be useful for practical farming implementation in irrigated high yielding environments.

show abstract

Section: Correlation Between Vegetation Indices Spad and Plant Heighcontrasting

confidence: 99%

Section: Grain Yield and Economic Return Responses To N Rates And Avamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments

2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…UAV-based applications in agronomy comprise biomass estimation via plant height measurements [17,18], LAI estimation [19,20], analysis of phenology [21] and yield prediction [22][23][24], amongst others.…”

mentioning

confidence: 99%

Towards Remote Estimation of Radiation Use Efficiency in Maize Using UAV-Based Low-Cost Camera Imagery

Tewes

Schellberg

2018

Agronomy

View full text Add to dashboard Cite

Radiation Use Efficiency (RUE) defines the productivity with which absorbed photosynthetically active radiation (APAR) is converted to plant biomass. Readily used in crop growth models to predict dry matter accumulation, RUE is commonly determined by elaborate static sensor measurements in the field. Different definitions are used, based on total absorbed PAR (RUE total ) or PAR absorbed by the photosynthetically active leaf tissue only (RUE green ). Previous studies have shown that the fraction of PAR absorbed (fAPAR), which supports the assessment of RUE, can be reliably estimated via remote sensing (RS), but unfortunately at spatial resolutions too coarse for experimental agriculture. UAV-based RS offers the possibility to cover plant reflectance at very high spatial and temporal resolution, possibly covering several experimental plots in little time. We investigated if (a) UAV-based low-cost camera imagery allowed estimating RUEs in different experimental plots where maize was cultivated in the growing season of 2016, (b) those values were different from the ones previously reported in literature and (c) there was a difference between RUE total and RUE green . We determined fractional cover and canopy reflectance based on the RS imagery. Our study found that RUE total ranges between 4.05 and 4.59, and RUE green between 4.11 and 4.65. These values are higher than those published in other research articles, but not outside the range of plausibility. The difference between RUE total and RUE green was minimal, possibly due to prolonged canopy greenness induced by the stay-green trait of the cultivar grown. The procedure presented here makes time-consuming APAR measurements for determining RUE especially in large experiments superfluous.

show abstract

Automatic zone management definition in Mediterranean environment for Triticum durum

Orsini,

Fiorentini,

Zenobi

et al. 2023

Agrosystems Geosci & Env

View full text Add to dashboard Cite

Precision agriculture (PA) is an agronomic management that allows the parsimonious use of agronomic inputs according to the crop's actual spatiotemporal needs. The most important agronomic practice of PA is the site‐specific management (SSM) of agronomic inputs. To apply SSM, the number and location of homogeneous zones must be defined. Nowadays, many approaches are used to define the number of zones in an arbitrary and constant way, such as high‐, medium‐, and low‐input zones, without a priori statistical analysis. Two combined field experiments were carried out on durum wheat (Triticum turgidum subsp. durum Desf.) in central Italy to agronomically validate an automatic approach aimed at defining the number and position of the homogeneous zones using multispectral images. For both sites, at stem elongation and anthesis, the dry biomass per square meter and Soil Plant Analysis Development (SPAD) readings were measured, while at crop maturity the yield was measured. This approach uses multispectral images as source data and uses the gap statistic index to determine the correct number of zones. The management zones created were well‐fitted with the experimental design of both sites, and the validation was made by the statistical analysis performed on yield data, SPAD, dry biomass, and normalized difference red edge index with an average difference of 40.2%, 28.5%, 57.5%, and 37.4%, respectively, between the management zones. The approach could be scaled by using the multitemporal multispectral images provided by satellite constellation, which enables farms and societies to take advantage of all the economic and environmental benefits of PA.

show abstract

Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service

Cited by 157 publications

References 38 publications

Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments

Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments

Towards Remote Estimation of Radiation Use Efficiency in Maize Using UAV-Based Low-Cost Camera Imagery

Automatic zone management definition in Mediterranean environment for Triticum durum

Contact Info

Product

Resources

About