Inter-relationships of cotton plant height, canopy width, ground cover and plant nitrogen status indicators

Muharam, Farrah Melissa; Bronson, K. F.; Maas, Stephen J.; Ritchie, Glen L.

doi:10.1016/j.fcr.2014.09.008

Cited by 33 publications

(19 citation statements)

References 40 publications

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“…Plant height can be obtained from the crop surface model (CSM), which is created by using structure-from-motion techniques. Several studies have already used CSM to estimate plant height and biomass for various crops, including maize [15–17], rice [18], barley [19, 20], cotton [21, 22], sugarcane [23], wheat [24] and sorghum [16, 25]. Previous studies have confirmed that combining spectral information and plant-height information can improve biomass estimates [1, 26–29].…”

Section: Introductionmentioning

confidence: 99%

Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

et al. 2019

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BackgroundAbove-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas.ResultsIn this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors.ConclusionsThese results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.Electronic supplementary materialThe online version of this article (10.1186/s13007-019-0394-z) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

et al. 2019

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“…Insufficient N fertilizer application causes premature senescence, while excessive application causes late ripening and increases environmental pollution. Recent studies have measured the impacts of N fertilization on cotton (Pettigrew and Adamczyk, 2006;Alitabar et al, 2012;Zhang et al, 2012;Muharam et al, 2014;Luo et al, 2018). Root growth is significantly affected by N fertilization, for example, Gaudin and Tian showed that low N levels enhanced root elongation (Tian et al, 2008;Gaudin et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Nitrogen Fertilization Increases Root Growth and Coordinates the Root–Shoot Relationship in Cotton

et al. 2020

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The root system plays an important role in the growth and development of cotton, and root growth is closely related to shoot growth, both of which are affected by N availability in the soil. However, it is unknown how N affects root growth and the rootshoot relationship under various N rates in the Yellow River Basin, China. Thus, the aim of this study was to assess the impacts of the application rate of N on root growth and the root-shoot relationship, to provide insight into the N regulation of root and shoot growth and N efficiency from the perspective of the root system. A field experiment conducted in 2014 and 2015 was used to determine the effects of N rates (0, 120, 240, and 480 kg ha −1) on root morphology, root distribution, the root-shoot relationship, and cotton yield. A moderate N fertilization rate (240 kg ha −1) increased root length, root surface area, and root biomass in most soil layers and significantly increased total root growth and total root biomass by more than 36.06% compared to the 0 kg ha −1 treatment. In addition, roots in the surface soil layers were more strongly affected by N fertilization than roots distributed in the deeper soil layers. Total root length, total root surface area, and root biomass in the 0-15 cm layer were significantly correlated with shoot biomass and boll biomass. In the 60-75 cm layer, total root length, total root surface area, and root length were significantly positively correlated with seed cotton yield. The application of a moderate level of N markedly increased total shoot biomass, boll biomass, and seed cotton yield. Our results show that increased shoot and boll biomasses were correlated with a significant increase in the root system especially the shallow roots in the moderate N treatment (240 kg ha −1), leading to an increase in cotton seed yield.

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“…In recent years, with the emergence of global environmental issues, rising awareness of global warming and the increased cost of nitrogen have spurred an interest in the investigation of nitrogen fertilization. A number of studies over the past few decades have investigated the effects of nitrogen on cotton growth [12][13][14]. These studies focused on the effects of nitrogen application on cotton leaves or on lint yield.…”

Section: Introductionmentioning

confidence: 99%

Nitrogen Fertilization Effects on Physiology of the Cotton Boll–Leaf System

et al. 2019

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The objective of this study was to assess the impacts of nitrogen on the physiological characteristics of the source–sink system of upper fruiting branches under various amounts of nitrogen fertilization. A two-year field experiment was conducted with a Bt cotton cultivar in the Yellow River Basin of China. The growth and yield of cotton of the upper fruiting branches were compared under four nitrogen levels: Control (N0, 0 kg ha−1), low nitrogen (N1, 120 kg ha−1), moderate nitrogen (N2, 240 kg ha−1), and high nitrogen (N3, 480 kg ha−1). The results indicated that in the subtending leaves in upper fruiting branches, chlorophyll content, protein content, and peroxidase (POD) activity dramatically increased with nitrogen application, reaching the highest under the moderate nitrogen treatment. The physiological characters in the seeds had the same trends as in the subtending leaves. Furthermore, the moderate nitrogen rate (240 kg ha−1) had a favorable yield and quality. Our results supported that a moderate nitrogen rate (240 kg ha−1) could coordinate the source–sink growth of cotton in the late stage, enhance the yield and fiber quality, and decrease the cost of fertilizer in the Yellow River Basin of China and other similar ecological areas.

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Inter-relationships of cotton plant height, canopy width, ground cover and plant nitrogen status indicators

Cited by 33 publications

References 40 publications

Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

Nitrogen Fertilization Increases Root Growth and Coordinates the Root–Shoot Relationship in Cotton

Nitrogen Fertilization Effects on Physiology of the Cotton Boll–Leaf System

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