Within-field nitrogen response in corn related to aerial photograph color

Williams, J. D.; Kitchen, Newell R.; Scharf, Peter C.; Stevens, W. E.

doi:10.1007/s11119-009-9137-x

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…In order to use aerial photography for predicting crop responses at early vegetative growth stages, high resolution digital cameras and/or lower flight elevation are recommended to improve image resolution and remove non-crop reflectance (Williams et al, 2010). According to Hunt et al (2003), airborne and satellite sensors can provide either high cost/high resolution imagery or low cost/low spatial resolution for use in precision agriculture.…”

Section: mentioning

confidence: 99%

Leaf nitrogen determination using non-destructive techniques–A review

Ali

Al-Ani

Eamus

et al. 2017

Journal of Plant Nutrition

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The optimisation of plant nitrogen-use-efficiency (NUE) has a direct impact on increasing crop production by optimising use of nitrogen fertiliser. Moreover, it protects environment from negative effects of nitrate leaching and nitrous oxide production. Accordingly, nitrogen (N) management in agriculture systems has been major focus of many researchers. Improvement of NUE can be achieved through several methods including more accurate measurement of foliar N contents of crops during different growth phases. There are two types of methods to diagnose foliar N status: destructive and non-destructive. Destructive methods are expensive and timeconsuming as they require tissue sampling and subsequent laboratory analysis. Thus, many farmers find destructive methods to be less attractive. Non-destructive methods are rapid and less expensive but are usually less accurate. Accordingly, improving the accuracy of non-destructive N estimations has become a common goal of many researchers, and various methods varying in

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Section: mentioning

confidence: 99%

Leaf nitrogen determination using non-destructive techniques–A review

Ali

Al-Ani

Eamus

et al. 2017

Journal of Plant Nutrition

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“…Nondestructive techniques have been investigated for estimating the Chl content of plants (Rigon et al 2012a). However, with the development of remote-sensing technology in recent years, image technologies have been used for potential real-time estimation of Chl content and subsequent analysis of photosynthetic properties (Williams et al 2010). Among the techniques, digital cameras are widely used together with the segmentation of images and color models (Li et al 2010) and are becoming a new quantitative tool in agriculture (Riccardi et al 2014).…”

Section: Introductionmentioning

confidence: 99%

A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis

Rigon¹,

Capuani²,

Fernandes³

et al. 2016

Photosynt.

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The development of smartphones, specifically their cameras, and imaging technologies has enabled their use as sensors/measurement tools. Here we aimed to evaluate the applicability of a fast and noninvasive method for the estimation of total chlorophyll (Chl), Chl a, Chl b, and carotenoids (Car) content of soybean plants using a smartphone camera. Single leaf disc images were obtained using a smartphone camera. Subsequently, for the same leaf discs, a Chl meter was used to obtain the relative index of Chl and the photosynthetic pigments were then determined using a classic method. The RGB, HSB and CIELab color models were extracted from the smartphone images and correlated to Chl values obtained using a Chl meter and by a standard laboratory protocol. The smartphone camera was sensitive enough to capture successfully a broad range of Chl and Car contents seen in soybean leaves. Although there was a variation between color models, some of the proposed regressions (e.g., the S and b index from HSB and Lab color models and NRI [RGB model]) were very close to the Chl meter values. Based on our findings, smartphones can be used for rapid and accurate estimation of soybean and Car contents in soybean leaves.

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“…In a few cases, there were substantial differences between the two rates due to errors in the programming of the applicator or errors in the GPS data. There were measureable yield differences among the three replications at each N rate within a management zone, often spanning several megagrams per hectare, which is typical for small‐plot yield data (Cerrato and Blackmer, 1990; Kwaw‐Mensah and Al‐Kaisi, 2006; Kyveryga et al, 2007; Williams et al, 2010). Yield variation was particularly great for the shoulder management zone in the Baker field, where yields differed by as much as 6 Mg ha −1 within a single N rate target.…”

Section: Resultsmentioning

confidence: 99%

Economically Optimal Nitrogen Rates of Corn: Management Zones Delineated from Soil and Terrain Attributes

2011

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Much of the NO 3 found in streams in the Midwest comes from leaching of N fertilizers applied to corn (Zea mays L.). To reduce this leaching, N fertilizer must be used more eff ectively. Dividing fi elds into areas that respond more uniformly to management is one approach for improving N fertilizer use. Th e objective of this study was to determine and compare economically optimal N fertilizer rates for corn in a corn-soybean [Glycine max (L.) Merr.] rotation within management zones. A method based on soil and landscape characteristics was used to divide three fi elds into management zones roughly equivalent to toeslope, footslope, backslope, and shoulder landscape positions. We applied N fertilizer at six rates within replicated small plots (15.1 by 4.5 m) located within each management zone. Yield vs. N rate for each management zone was fi tted to the Mitscherlich equation using nonlinear methods. Final Mitscherlich parameters, economically optimal N rates, and their confi dence limits were computed for each management zone. Yields at the highest N rate generally followed the pattern of toeslope ≥ footslope ≥ backslope >> shoulder among the management zones. Conversely, economically optimal N rates followed the reverse pattern and varied from 23 to 247 kg ha -1 . Th us, yield at the highest N rate was a poor predictor of the optimal N rate for the management zones delineated in this study and N fertilizer application would have been optimized by applying relatively less to the toeslope and footslope management zones and relatively more to the shoulder and backslope management zones.

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Within-field nitrogen response in corn related to aerial photograph color

Cited by 12 publications

References 27 publications

Leaf nitrogen determination using non-destructive techniques–A review

Leaf nitrogen determination using non-destructive techniques–A review

A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis

Economically Optimal Nitrogen Rates of Corn: Management Zones Delineated from Soil and Terrain Attributes

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