A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data

Chen, Yuanyuan; Sun, Ling; Pei, Zhiyuan; Sun, Jianbao; He, Li; Jiao, Weijie; You, Jiong

doi:10.3390/s22030989

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…Lodging is one of the common phenomena in maize production, which is mainly caused by strong wind and rainstorms. Remote sensing technology has been widely used in maize lodging monitoring, including optical remote sensing [36] [44], Radar remote sensing [29] [35]and UAV remote sensing [6] [15]. Previous researches mainly focused on monitoring the range of maize lodging [36] [37].…”

Section: Discussionmentioning

confidence: 99%

“…At present, optical satellites images have been applied in crop lodging monitoring. Chen et al [36] proposed a spectral sum index to identify the lodging range of large-scale maize crop by analyzing the spectral changes of non-lodging and lodging maize crop. These studies [36] [37] mainly focused on the monitoring of lodging range of crops.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [36] proposed a spectral sum index to identify the lodging range of large-scale maize crop by analyzing the spectral changes of non-lodging and lodging maize crop. These studies [36] [37] mainly focused on the monitoring of lodging range of crops. Therefore, the evaluation of large-scale lodging extents is of great significance.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Lodging Extents of Maize Crop Using Multitemporal GF-1 Images

Shi

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Maize crop lodging is a recurrent phenomenon which results in significant reduction of grain yield and quality in addition to the impediment of mechanical harvesting. The large-scale monitoring of maize crop lodging is important for production policy adjustment and agricultural insurance compensation. In this study, we derived a variety of features from multi-temporal GaoFen-1(GF-1) images before and after maize crop lodging. We screened the most sensitive features of the spectrum, texture, and vegetation index to monitor maize crop lodging. The recursive feature elimination method based on cross-validation (RFECV) and Mutual information (MI) were compared to obtain the optimal feature combination for monitoring the lodging extents of maize crop. The random forest (RF) classifier was used to classify the lodging extents. The results showed that the most sensitive features of the spectrum, texture, and vegetation indices of lodging extents included the difference of reflectance in blue, green and red bands, the difference of normalized difference vegetation index, the difference of ratio vegetation index, the difference of enhanced vegetation index difference, the difference of mean value of blue band, the difference of mean value of green band, and the difference of mean value of red band. The total accuracy of lodging extents classification was 87.50%, and the Kappa coefficient was 0.83 for testing samples. Based on multiple features derived from GF-1 images before and after lodging, the lodging extents of maize crop can be monitored on a large scale.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring Lodging Extents of Maize Crop Using Multitemporal GF-1 Images

Shi

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Crop lodging detection is mainly used for disaster assessment and field management in existing research. Compared with traditional manual detection, typical lodging detection techniques mainly rely on sensors such as visible light, multispectral, hyperspectral, near-infrared, and radar [4] sensors on satellite [5], radar [6], and unmanned aerial vehicles (UAVs) [7]. The sensitivity of crop features such as texture, color, and vegetation index under single/multiple growth stages with a high-throughput and large field of view to lodging data is analyzed to recognize and classify lodging areas [8,9].…”

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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