Effect of spring irrigation on soil salinity monitoring with UAV-borne multispectral sensor

Yang, Ning; Cui, Weihong; Zhang, Zhitao; Zhang, Junrui; Chen, Junying; Ma, Yu; Lao, Congcong; Song, Zhishuang; Chen, Yinwen

doi:10.1080/01431161.2021.1978579

Cited by 22 publications

(23 citation statements)

References 48 publications

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“…The Kenli District is located at the mouth of the Yellow River and forms a typical alluvial fan delta (Yang, Cai, et al, 2021; Yang, Lv, et al, 2021; Yang, Ma, et al, 2021; Yang, Yang, et al, 2021). It belongs to a continental monsoon climate zone, with obvious temperature differences in the four seasons, making it suitable for crop growing.…”

Section: Methodsmentioning

confidence: 99%

“…UAV technology is developing rapidly and is widely used, providing a new high‐precision remote sensing data sources with excellent properties (Ivushkin et al, 2017). Despite their inability to cover large areas, lightweight UAVs can collect multispectral data with centimeter‐to‐decimeter spatial resolution (Yang, Cai, et al, 2021; Yang, Lv, et al, 2021; Yang, Ma, et al, 2021; Yang, Yang, et al, 2021). They are more flexible than space‐borne sensors and can acquire data simultaneously with satellites (Alvarez et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The NMF method has strong advantages in fusing hyperspectral and multispectral data. It can properly select the dimension of feature space and obtain local and spatial details of original data to obtain fusion images with excellent spectral performance and detail retention, which has been widely applied in remote sensing data fusion (Chen et al, 2014; Yang, Cai, et al, 2021; Yang, Lv, et al, 2021; Yang, Ma, et al, 2021; Yang, Yang, et al, 2021). Differential coefficients can obtain different correction coefficients according to the differences between samples, so as to capture heterogeneous relations and achieve more accurate fusion (Madonsela et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning inversion models have been applied in several studies and were shown to be better than traditional statistical models (Lu et al, 2021; Zhou et al, 2020). Machine learning methods such as extreme gradient‐boosting (XGBoost) (Nguyen et al, 2021), extreme learning machine (ELM) (Guo et al, 2021), random forest (RF) (Tao et al, 2021), and CNN (Yang, Cai, et al, 2021; Yang, Lv, et al, 2021; Yang, Ma, et al, 2021; Yang, Yang, et al, 2021) show great potential in soil salinity monitoring. To improve the accuracy and reliability of the inversion model, the satellite‐UAV‐ground integrated method of monitoring soil salinity using machine learning models needs to be further explored.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

Chang

Yang

et al. 2022

Land Degrad Dev

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Soil salinity is a crucial factor in agriculture, rising salinity undermines cotton (Gossypium spp.) production in coastal areas of China and damages crops in other countries. In this study, we propose an effective integration method using satellite‐ground spectral fusion and satellite‐unmanned arial vehicle (UAV) collaboration for soil salinity monitoring in cotton growing areas. Firstly, an extreme learning machine (ELM), random forest (RF), and extreme gradient boosting (XGBoost) models were constructed based on UAV images from test areas. The optimal model was selected for soil salinity inversion. Meanwhile, ground imaging hyperspectrum and SENTINEL‐2A multispectral images were differentially fused by nonnegative matrix factorization (NMF). Then, taking the inversion results of UAV as the training sample to build convolutional neural network (CNN) model of the fused SENTINEL‐2A, the soil salinity distribution map of cotton fields in the study area was obtained by inversion, and the satellite‐UAV‐ground integrated inversion of soil salinity in coastal cotton fields was realized. The results showed that the spectrum after satellite‐ground fusion was closer to the original ground hyperspectrum, the fusion improved the correlation between spectrum and soil salinity, and UAV inversion data possessed great potential for the reference data of satellite inversion. The soil salinity obtained by satellite‐UAV‐ground integration approach was highly consistent with the measured salinity in the study area (R2 = 0.805), and the integration approach is suitable for soil salinity inversion in the cotton seedling stage in the coastal area. The satellite‐UAV‐ground integration approach proposed in this study fully tap the advantages of remote sensing data from different platforms and improved the ability to obtain soil salinisation information in large‐scale quantitatively, accurately, and quickly.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

Chang

Yang

et al. 2022

Land Degrad Dev

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“…With the unique characteristics of high flexibility and maneuverability, unmanned aerial vehicles (UAVs) stand out among various Agri-robots and have proven to be a powerful platform to conduct field surveys and aerial management [6][7][8]. For example, UAVs have been widely used in different agricultural applications, such as soil mapping, crop identification, and biotic/abiotic monitoring [9][10][11][12][13]. Specifically, UAV-based agrochemical spraying has drawn extensive attention in the agricultural community [14,15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of Parameters of a Spraying System Designed for UAV Application on the Spraying Quality Based on Box–Behnken Response Surface Method

Wang

et al. 2022

Agriculture

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With the development of precision agriculture (PA), low-altitude and low-volume spraying based on unmanned aerial vehicles (UAVs) is playing an increasingly important role in the control of crop diseases, pests, and weeds. However, the aerial spraying quality and droplet drift are affected by many factors, some of which are controllable (e.g., flight and spraying parameters) and some of which are not (e.g., environmental parameters). In order to study the influence of spraying parameters on the UAV-based spraying performance, we propose a UAV-compatible spraying system and a customized experimental platform in this work. Through single-factor test and Box–Behnken response surface methods, four influencing factors, namely spraying height, flow rate, distance between nozzles, and pulse width modulation (PWM) duty cycle, were studied under indoor conditions. Variance analysis and multiple quadratic regression fitting were performed on the test data by using Design-Expert 8.0.5B software, and quadratic polynomial regression models of effective spraying width, droplet coverage density, coefficient of variation, and droplet coverage rate were established. Based on the Z-score standardization, a mathematical model of the comprehensive score with four factors was established to evaluate the spraying quality and predict optimal spraying parameters. Test results indicate that the effect intensity of four influencing factors from strong to weak is PWM duty cycle, flow rate, distance between nozzles, and spraying height, and their optimal values are 98.65%, 1.74 L/min, 1.0 m, and 1.60 m, respectively. Additionally, verification experimental results demonstrate that the deviation between the predicted comprehensive score and the actual value was less than 6%. This paper can provide a reference for the design and optimization of UAV spraying systems.

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UAV image acquisition and processing for high‐throughput phenotyping in agricultural research and breeding programs

Bongomin,

Lamo,

Guina

et al. 2024

The Plant Phenome Journal

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We are in a race against time to combat climate change and increase food production by 70% to feed the ever‐growing world population, which is expected to double by 2050. Agricultural research plays a vital role in improving crops and livestock through breeding programs and good agricultural practices, enabling sustainable agriculture and food systems. While advanced molecular breeding technologies have been widely adopted, phenotyping as an essential aspect of agricultural research and breeding programs has seen little development in most African institutions and remains a traditional method. However, the concept of high‐throughput phenotyping (HTP) has been gaining momentum, particularly in the context of unmanned aerial vehicle (UAV)‐based phenotyping. Although research into UAV‐based phenotyping is still limited, this paper aimed to provide a comprehensive overview and understanding of the use of UAV platforms and image analytics for HTP in agricultural research and to identify the key challenges and opportunities in this area. The paper discusses field phenotyping concepts, UAV classification and specifications, use cases of UAV‐based phenotyping, UAV imaging systems for phenotyping, and image processing and analytics methods. However, more research is required to optimize UAVs’ performance for image data acquisition, as limited studies have focused on the effect of UAVs’ operational parameters on data acquisition.

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Effect of spring irrigation on soil salinity monitoring with UAV-borne multispectral sensor

Cited by 22 publications

References 48 publications

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

Analysis of the Influence of Parameters of a Spraying System Designed for UAV Application on the Spraying Quality Based on Box–Behnken Response Surface Method

UAV image acquisition and processing for high‐throughput phenotyping in agricultural research and breeding programs

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