Identifying Corn Lodging in the Mature Period Using Chinese GF-1 PMS Images

Huang, Xianda; Fu, Xuan; Dong, Yi; Su, Wei; Wang, Xinsheng; Huang, Jianxi; Li, Xuecao; Zeng, Yelu; Miao, Shuangxi; Li, Jiayu

doi:10.3390/rs15040894

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…Although Chu et al (2017) did record red, green and blue (RGB) and NIR imagery, comparisons to vegetation indices such as NDRE and NDVI were not made. Huang et al (2023) did observe strong relationships between lodging and various vegetation indices extracted from satellite images of corn categorized into three lodging severity categories (non-lodged, moderately, or severely lodged), with NDVI being ranked as of high importance in the initial screening.…”

Section: Red Edge Reflectancementioning

confidence: 86%

“…Huang et al. (2023) did observe strong relationships between lodging and various vegetation indices extracted from satellite images of corn categorized into three lodging severity categories (non‐lodged, moderately, or severely lodged), with NDVI being ranked as of high importance in the initial screening.…”

Section: Relationship Of Spectral Bands To Height Yield and Grain Qua...mentioning

confidence: 99%

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Quantification of root lodging damage in corn using uncrewed aerial vehicle imagery

Lindsey¹,

Allred²,

Martinez³

et al. 2023

Crop Forage & Turfgrass Mgmt

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Accurate quantification of damage associated with root lodging events can help producers assess damage, predict potential yield losses, and help understand potential issues with grain quality that may arise post‐harvest (i.e. kernel weight reductions, premature germination on the ear or vivipary). The objective of this research was to utilize imagery from an uncrewed aerial vehicle (UAV) to accurately quantify crop canopy height, grain yield, and identify trends in imagery data associated with grain quality after root lodging was imposed at multiple growth stages. Simulated corn root lodging experiments were conducted in 2018 and 2019 with lodging treatments applied at two vegetative or two reproductive growth stages (V10, V14, VT/R1, and R3). At dough stage (R4), visible‐color and multispectral images were collected from each trial. Bare fields were also flown in February to obtain baseline elevation data. Imagery data were used to develop digital surface model (DSM) images and used to calculate indices of normalized difference red edge (NDRE) and normalized difference vegetation index (NDVI). Individual datapoints within each experimental plot were extracted from the imagery files and were compared to ground‐truth measurements. The DSM height values were similar to actual measured heights for most lodging treatments (Adj. R2 = .957). Both NDRE and NDVI exhibited linear trends with height and quality parameters (Adj. R2 = .25‐.54), though yield patterns were best described using a quadratic model (Adj. R2 = .42‐.60). These procedures hold utility in accurately quantifying canopy height following a root lodging event, and hold promise in helping consultants identify yield and grain quality reductions associated with root lodging.This article is protected by copyright. All rights reserved

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Section: Red Edge Reflectancementioning

confidence: 86%

Section: Relationship Of Spectral Bands To Height Yield and Grain Qua...mentioning

confidence: 99%

Quantification of root lodging damage in corn using uncrewed aerial vehicle imagery

Lindsey¹,

Allred²,

Martinez³

et al. 2023

Crop Forage & Turfgrass Mgmt

View full text Add to dashboard Cite

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“…GF-1 launched on 26 April 2013, which is the first satellite of the China High-resolution Earth Observation System (CHEOS) project, equipped with panchromatic and multispectral cameras, short revisit cycles, multiple available times and phases, and plays an important role in agricultural resource surveys and marine environmental monitoring [30][31][32][33][34]. On 28 June 2019, China's independent research marine satellite "HY-1C" was officially put into use, and in June of the following year, "HY-1D" was officially put into use.…”

Section: Introductionmentioning

confidence: 99%

Twin Satellites HY-1C/D Reveal the Local Details of Astronomical Tide Flooding into the Qiantang River, China

Cai,

Zhang,

et al. 2024

Remote Sensing

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This article extracts the Qiantang River tidal bore, analyzing the water environment characteristics in front of the tidal line of the Qiantang River tidal bore and behind it. The Qiantang River tidal bore Index (QRI) was established using HY-1C, HY-1D, and Gao Fen-1 wide field-of-view (GF-1 WFV) satellite data to precisely determine the location and details of the Qiantang River tidal bore. Comparative analyses of the changes on the two sides of the Qiantang River tidal bore were conducted. The results indicate the following: (1) QRI enhances the visibility of tidal bore lines, accentuating their contrast with the surrounding river water, resulting in a more vivid character. QRI proves to be an effective extraction method, with potential applicability to similar tidal lines in different regions. (2) Observable roughness changes occur at the tidal bore location, with smoother surface textures observed in front of the tidal line compared to those behind it. There is a discernible increase in suspended sediment concentration (SSC) as the tidal bore passes through. (3) This study reveals the mechanism of water environment change induced by the Qiantang River tidal bore, emphasizing its significance in promoting vertical water body exchange as well as scouring the bottom sediments. This effect increases SSC and surface roughness.

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“…Compared with traditional image processing and deep learning, the machine learning method strikes a balance by focusing on both features and processing. After extracting and purifying sensitive features for lodging, recognition models such as support vector machine (SVM), random forest (RF), naive Bayesian (NB), backpropagation (BP), maximum likelihood method (MLC), and Gaussian process regression (GPR) [27][28][29] are constructed for detection. The model construction reduces the dependence of detection accuracy on the sensitivity of selected features.…”

Section: Introductionmentioning

confidence: 99%

Wheat Lodging Direction Detection for Combine Harvesters Based on Improved K-Means and Bag of Visual Words

Zhang,

Chen,

et al. 2023

Agronomy

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For the inconsistent lodging of wheat with dense growth and overlapped organs, it is difficult to detect lodging direction accurately and quickly using vehicle vision for harvesters. Therefore, in this paper, the k-means algorithm is improved by designing a validity evaluation function, selecting initial clustering centers by distance, constructing a multidimensional feature vector, and simplifying calculations using triangle inequality. An adaptive image grid division method based on perspective mapping and inverse perspective mapping with a corrected basic equation is proposed for constructing a dataset of wheat lodging directions. The improved k-means algorithm and direction dataset are used to construct a bag of visual words. Based on scale-invariant feature transform, pyramid word frequency, histogram intersection kernel, and support vector machine, the wheat lodging directions were detected in the grid. The proposed method was verified through experiments with images acquired on an intelligent combine harvester. Compared with single-level word frequencies with existing and improved k-means, the mean accuracy of wheat lodging direction detection by pyramid word frequencies with improved k-means increased by 6.71% and 1.11%, respectively. The average time of detection using the proposed method was 1.16 s. The proposed method can accurately and rapidly detect wheat lodging direction for combine harvesters and further realize closed-loop control of intelligent harvesting operations.

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Identifying Corn Lodging in the Mature Period Using Chinese GF-1 PMS Images

Cited by 7 publications

References 38 publications

Quantification of root lodging damage in corn using uncrewed aerial vehicle imagery

Quantification of root lodging damage in corn using uncrewed aerial vehicle imagery

Twin Satellites HY-1C/D Reveal the Local Details of Astronomical Tide Flooding into the Qiantang River, China

Wheat Lodging Direction Detection for Combine Harvesters Based on Improved K-Means and Bag of Visual Words

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