Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

Ballester, Carlos; Hornbuckle, John; Brinkhoff, James; Smith, John A.; Quayle, Wendy

doi:10.3390/rs9111149

Cited by 70 publications

(53 citation statements)

References 31 publications

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“…The CCCI has been reported to correlate well with plant N in wheat and durum wheat (Bronson et al., 2017a; Cammarano et al., 2011) and in cotton (Barnes et al., 2000). Recent reports with cotton indicated that CCCI performed as well as (Ballester et al., 2017) or better than NDRE (Raper & Varco, 2015) in estimating cotton N status in the field. Raper and Varco (2015) state that CCCI standardizes for biomass and would be effective for early‐season N assessment.…”

Section: Discussionmentioning

confidence: 97%

“…The NDRE is not nearly as well‐studied in the literature as the NDVI, but its use in agricultural research is increasing (Ballester, Hornbuckle, Brinkhoff, Smith, & Quayle, 2017; Barnes et al., 2000; Bean et al., 2018; Long et al., 2009; Montealegre, Wortman, Fergusan, Shaver, & Schepers, 2019; Raper & Varco, 2015; Shiratsuchi et al., 2011; Stamatiadis et al., 2019). Shiratsuchi et al.…”

Section: Discussionmentioning

confidence: 99%

“…Ballester et al. (2017) recently employed a 5‐band RedEdge, MicaSense camera, with visible, NIR, and red edge wavebands on an unmanned aerial vehicle to assess cotton N status in Australia. They reported that CCCI and NDRE were the VIs tested that best distinguished N treatments.…”

Section: Discussionmentioning

confidence: 99%

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Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

et al. 2020

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Use of active optical sensors (AOS) in nitrogen (N) management of row crops continues to grow. Since the first studies in the mid-1990s, several commercial AOS are now available. Typically, canopy reflectance in red and near infrared (NIR) bands are used to calculate the normalized difference vegetation index (NDVI). More recently, commercially available AOS include a third, red-edge band that allows the calculation of additional vegetation indices (VIs). We present two studies of five site-years of N management studies in Maricopa, AZ, on a Casa Grande sandy loam with cotton (Gossypium hirsutum L.). The 2014-2015 study was conducted under an overhead sprinkler irrigation system (OSI), and the 2016-2018 study was in subsurface drip irrigation (SDI). The study objective was to compare the ability of 12 different VIs to detect N deficiency among N treatments from shortly after emergence to midbloom. In the OSI study, which showed delayed, small N treatment effects, NDRE and the chlorophyll index using amber (CIRE) detected N deficiency in zero-N and in reduced N-reflectance-based plots 7-23 d before other VIs did. With SDI, the choice of VI was less critical as several VIs could distinguish N deficiency in zero-N and in reflectance plots. The commonly used NDVI red (NDVIR) only detected N deficiency in reflectance plots in one of five site-years. In conclusion, we recommend the use of AOS with NIR and red-edge bands and the calculation of NDRE or CIRE to guide AOS-based in-season N management of irrigated cotton.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

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Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

et al. 2020

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“…Vegetation biomass [22,103] nitrogen status [22,99,103,110] moisture content [109,110] vegetation color [49,54] spectral behavior of chlorophyll [64,99] temperature [64,69] spatial position of an object [32,106] size and shape of different elements and plants vegetation indices [54][55][56] Soil moisture content [109,112] temperature [66,69] electrical conductivity [66] With the use of specialized sensors, UAVs can acquire information for various features of the cultivated field. However, as mentioned above, there is still no standardized workflow or well established techniques to follow for analyzing and visualizing the information acquired.…”

Section: Crop Featuresmentioning

confidence: 99%

“…Regression has been used to estimate spectral vegetation indices by analyzing data acquired from RGB images [35], presenting generally good results. Additionally, regression has been used to examine the correlation of some vegetation indices with vegetation features such as nitrogen [22,38,99], leaf are index [36,103], and biomass [22,36,103]. For this purpose, both linear (simple and multiple) regression and nonlinear regression methods have been used.…”

Section: Using Machine Learningmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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Coal char effects on soil chemical properties and maize yields in semi‐arid region

Panday

Mikha

Sun

et al. 2021

Agrosystems Geosci & Env

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Soil amendments with high carbon (C) content can be effective in semi‐arid regions where soils are characterized by low C. A field study was conducted in 2016–2018 to evaluate the effect of char on soil chemical properties and irrigated maize (Zea mays L.) yields in sandy loam fertilized with urea or composted manure. Carbon‐rich char used was a product of coal combustion residue from a local factory in western Nebraska. The experiment was arranged in a split‐plot randomized complete block design in four replications with char (0, 6.7, 13.4, 20.1, and 26.8 Mg C ha−1) as main and N treatment (0, 90, 180, and 270 kg urea‐N ha−1 and 33.6 and 67.2 Mg ha−1 of composted manure) as subplot factors. A handheld spectral sensor was used to determine normalized difference red edge (NDRE) at growth stages (V6, V8, V10, and R1) in 2017 and 2018. After 2 yr, char increased Fe, reduced pH at lower rates, and increased K and Mg at higher rates in top 20 cm soil but did not affect crop yields. Char applied at ≥13.4 Mg C ha−1 increased soil organic C by ≥8% and composted manure increased soil P and K compared to the control. There was a strong correlation of NDRE with N rates and grain yields at V8 and V10. This study found no adverse effect of char on soil properties. However, more site‐specific research is needed before char can be used as a regular soil amendment in semi‐arid regions.

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Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

Cited by 70 publications

References 31 publications

Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

A Review on UAV-Based Applications for Precision Agriculture

Coal char effects on soil chemical properties and maize yields in semi‐arid region

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