Describing the Height Growth of Corn Using Logistic and Gompertz Model

Wardhani, Wayan Surya; Kusumastuti, Prawitra

doi:10.17503/agrivita-2013-35-3-p237-241

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…These and previous UAS surveys of plant height captured appeared as a sigmoidal growth pattern (Fig. 3), which is commonly applied to plant growth (Archontoulis and Miguez, 2015; Wardhani and Kusumastuti, 2013). While these data are highly informative, completing UAS surveys more than 20 times in a season is resource intensive and impractical, and the data can be redundant (assuming quality data are collected on every flight) for some dates.…”

Section: Resultsmentioning

confidence: 57%

Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems

Anderson

Murray

Malambo

et al. 2019

The Plant Phenome Journal

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UAS captured increased genetic variation compared with manual terminal height. There were small significant differences in ground filtering methods to extract plant structure. Higher resolution did not improve imagery informativeness with regard to plant height. Logistic function provides informative phenotypes for temporal maize growth. Correlation and prediction accuracy of grain yield increased by ∼20% with UAS heights. Weekly unmanned aerial system (UAS) imagery was collected over the College Station, TX, 2017 Genomes to Fields (G2F) hybrid trial, across three environmental stress treatments, using two UAS platforms. The high‐altitude (120‐m) fixed‐wing platform increased the fraction of variation attributed to genetics and had highly repeatable (R > 60%) height estimates, increasing the genetic variance explained (10–40%) over traditional terminal plant height measurement (PHTTRML ∼30%), as well as over the low‐altitude rotary‐wing UAS platform (10–20%). A logistic function reduced the dimensionality (>20 flights) of each UAS dataset to three parameters (inflection point, growth rate, and asymptote) and produced a more robust predictive model than independent flight dates, effectively summarizing (R2 > 0.98) the UAS flight dates. The logistic model overcame the need to use specific flight dates when comparing different environments. The UAS height estimates (r = 0.36–0.48) doubled the correlations to grain yield in this G2F experiment compared with PHTTRML (r = 0.23–0.28). Parameters of the logistical function achieved equivalent correlations (r = 0.30–0.46) to individual flight dates (r = 0.36–0.48), improving grain yield prediction by ∼400% (R2 = 0.25–0.34) over PHTTRML (R2 = 0.06–0.08). Incorporating other UAS‐derived parameters beyond plant height may allow yield to be accurately predicted before maturity, speeding breeding programs. A new public R function to generate ESRI shapefiles for plot research is also described.

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Section: Resultsmentioning

confidence: 57%

Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems

Anderson

Murray

Malambo

et al. 2019

The Plant Phenome Journal

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“…Plant height is a plant size often observed as an indicator of growth as well as a parameter to measure environmental influences or treatments applied because plant height is the most easily measured growth measure (Wardhani & Kusumastuti, 2014). Sweet corn plants with the Tithonia diversifolia litter treatment have the best plant growth compared to those with other treatments.…”

Section: Discussionmentioning

confidence: 99%

Application of litters to inhibit nitrification in Vertisols on sweet corn (Zea mays S.)

Supriyadi

Widyatama²,

Prinandhika³

et al. 2021

Sains Tanah J. Soil Sci. Agroclimatology

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Nitrification, or the process of oxidation of ammonium to nitrate in the soil, needs to be inhibited because it reduces the efficiency of nitrogen fertilizers. Vertisols have 2:1 minerals and have high negative charge, so ammonium is more absorbed by soil particles, whereas nitrate is free to move in the soil and diffuses into the plant tissue or is leached with gravity water. This study aimed to determine the litter treatment that can inhibit the nitrification process in Vertisols on sweet corn plants. This research was conducted in June until November 2019 in the Plastic House of Plesungan, Gondangrejo, Karanganyar, Indonesia. This study used a basic completely randomized design with a single factor (litter type) as an immobilizer. The types of litter used in this study were Gliricidia maculata, Albizia falcataria, Senna siamea, and Tithonia diversifolia. The parameters observed were ammonium content, nitrification potential, average nitrate content, actual nitrification, plant height, number of leaves, and dry crown plant. Tithonia diversifolia gave the highest actual nitrification of 23.26%. Senna siamea has the lowest actual nitrification of 12.36%, followed by Gliricidia maculata with 17.39% and Albizia falcataria with 17.67%. This shows that the Tithonia diversifolia litter has the highest value in inhibiting nitrification. Maize plants treated with the Tithonia diversifolia litter had the best plant growth compared to those applied with other treatments. Therefore, among the treatments used, the Tithonia diversifolia litter was most optimal in inhibiting nitrification in Vertisols.

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“…which is widely applied to agriculture. The growth model is used to determine the relationship between product growth and time (Wardhani & Kusumastuti, 2013). The plant growth model that forms the S curve is called the Sigmoid growth model.…”

Section: A Introductionmentioning

confidence: 99%

Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration

Gultom

Solimun

Nurjannah

2022

JTAM

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Soybean plants have limited growth with a planting period of 12 weeks, which causes the observed sample to be very small. A small sample of soybean plant growth observations can be bias causes in the conclusion of prediction results on soybean plant growth. The purpose this study is to apply the bootstrap resampling technique in Gompertz growth model which overcomes residual distribution with small samples, the research data was taken from soybean plant growth in four varieties with four spacing treatments, five replications and twelve weeks (long planting period). Gompertz growth model uses nonlinear least squares method in estimating parameters with Levenberg–Marquardt iteration. The value of the Gompertz model after resampling bootstrap has no significant difference. The adjusted R2 value of 0.96 is close to 1. This means that the total diversity of plant heights can be explained by the Gompertz model of 96 percent. Judging from the graph of predictions of soybean plant growth before resampling and after resampling coincide with each other it can also be seen in the initial growth values before resampling 14, 05 and 14.18, the maximum growth values are 55.13 and 55.60. Bootsrap resampling technique can overcome residual normality in the Gompertz growth model, but does not change the information in the initial data.

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Describing the Height Growth of Corn Using Logistic and Gompertz Model

Cited by 13 publications

References 8 publications

Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems

Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems

Application of litters to inhibit nitrification in Vertisols on sweet corn (Zea mays S.)

Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration

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