Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model

Cheng, Zhiqiang; Meng, Jun; Shang, Jiali; Liu, Jiangui; Huang, Jianxi; Qiao, Yunfeng; Qian, Budong; Jing, Qi; Dong, Ting; Yu, Lihong

doi:10.3390/s20216006

Cited by 14 publications

(9 citation statements)

References 51 publications

(67 reference statements)

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“…Tiller number counting could contribute to estimating current crop partitioning parameters by combining other observables such as biomass. The growth-model prediction could enhance accuracy through data assimilation with phenotyping results [ 32 , 33 ]. Data-assimilation methods with panicles and tillers could be further studied.…”

Section: Discussionmentioning

confidence: 99%

Image-Based Phenotyping for Non-Destructive In Situ Rice (Oryza sativa L.) Tiller Counting Using Proximal Sensing

Yamagishi

Kato

Ninomiya

et al. 2022

Sensors

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The increase in the number of tillers of rice significantly affects grain yield. However, this is measured only by the manual counting of emerging tillers, where the most common method is to count by hand touching. This study develops an efficient, non-destructive method for estimating the number of tillers during the vegetative and reproductive stages under flooded conditions. Unlike popular deep-learning-based approaches requiring training data and computational resources, we propose a simple image-processing pipeline following the empirical principles of synchronously emerging leaves and tillers in rice morphogenesis. Field images were taken by an unmanned aerial vehicle at a very low flying height for UAV imaging—1.5 to 3 m above the rice canopy. Subsequently, the proposed image-processing pipeline was used, which includes binarization, skeletonization, and leaf-tip detection, to count the number of long-growing leaves. The tiller number was estimated from the number of long-growing leaves. The estimated tiller number in a 1.1 m × 1.1 m area is significantly correlated with the actual number of tillers, with 60% of hills having an error of less than ±3 tillers. This study demonstrates the potential of the proposed image-sensing-based tiller-counting method to help agronomists with efficient, non-destructive field phenotyping.

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Section: Discussionmentioning

confidence: 99%

Image-Based Phenotyping for Non-Destructive In Situ Rice (Oryza sativa L.) Tiller Counting Using Proximal Sensing

Yamagishi

Kato

Ninomiya

et al. 2022

Sensors

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“…Although the accuracy of the results from these studies was acceptable, an increase in variety of input of variables could improve the accuracy of the models further (Khaliq et al, 2018;Fu et al, 2020;Zhou et al, 2020). Cheng et al (Cheng et al, 2020) determined the LAI of maize based on VI, crop models, and data assimilation methods, achieving improved accuracy compared to any single method. Shu et al (Meiyan et al, 2022) constructed a prediction model for the aboveground biomass of maize in multiple growth periods by combining multispectral and UAV digital images with maize LAI and plant height.…”

Section: Introductionmentioning

confidence: 99%

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

Xie

Chen²,

Yu³

et al. 2023

Front. Plant Sci.

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IntroductionCanopy stomatal conductance (Sc) indicates the strength of photosynthesis and transpiration of plants. In addition, Sc is a physiological indicator that is widely employed to detect crop water stress. Unfortunately, existing methods for measuring canopy Sc are time-consuming, laborious, and poorly representative.MethodsTo solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to predict the Sc values and used citrus trees in the fruit growth period as the research object. To achieve this, VI and texture feature data of the experimental area were obtained using a multispectral camera. The H (Hue), S (Saturation) and V (Value) segmentation algorithm and the determined threshold of VI were used to obtain the canopy area images, and the accuracy of the extraction results was evaluated. Subsequently, the gray level co-occurrence matrix (GLCM) was used to calculate the eight texture features of the image, and then the full subset filter was used to obtain the sensitive image texture features and VI. Support vector regression, random forest regression, and k-nearest neighbor regression (KNR) Sc prediction models were constructed, which were based on single and combined variables.ResultsThe analysis revealed the following: 1) the accuracy of the HSV segmentation algorithm was the highest, achieving more than 80%. The accuracy of the VI threshold algorithm using excess green was approximately 80%, which achieved accurate segmentation. 2) The citrus tree photosynthetic parameters were all affected by different water supply treatments. The greater the degree of water stress, the lower the net photosynthetic rate (Pn), transpiration rate (Tr), and Sc of the leaves. 3) In the three Sc prediction models, The KNR model, which was constructed by combining image texture features and VI had the optimum prediction effect (training set: R2 = 0.91076, RMSE = 0.00070; validation set; R2 = 0.77937, RMSE = 0.00165). Compared with the KNR model, which was only based on VI or image texture features, the R2 of the validation set of the KNR model based on combined variables was improved respectively by 6.97% and 28.42%.DiscussionThis study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology. Moreover, it can be used to monitor the dynamic changes of Sc and provide a new technique for gaining a better understanding of the growth status and water stress of citrus crops.

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“…In recent years, a large number of researchers have conducted studies in the eld of remote sensing for agricultural products (Zhu et al, 2019;Zhang et al, 2021), chlorophyll content (Zhang et al, 2021;Xie et al, 2019), water and nitrogen content (Thorp et al, 2018;Marang et al, 2021). Many studies have also focused on remote sensing tools, such as hand-held spectrometers (Dong et al, 2019), drones (Cheng et al, 2020), and satellites (Fang et al, 2015;Dong et al, 2020). Studies have shown that satellite images have good potential in estimating the leaf area index (Gaso et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Landsat 9 and Sentinel 2 satellites in estimation of cotton leaf area index using M5 tree model

Mazidi,

Hessam,

Khalili

et al. 2023

Preprint

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The availability of satellite data has caused a continuous increase in the accuracy of spatial information and provides significant conditions for monitoring cultivation in large areas. LAI is used as an important parameter using remote sensing techniques for simple crop growth modeling. In this article, estimation of LAI, as a common vegetation cover index, through the collection and field measurements of LAI, and comparison with the multispectral data of Landsat 9 operational terrain imager (OLI) and Sentinel 2 multispectral instrument were done with the minimum percentage of clouds to extract LAI. The relationship between plant indices as an independent variable and field LAI as a dependent variable was searched using linear multivariate regression and M5 tree regression methods. LAI calculated with plant indices is not very accurate and needs to be modeled and recalculated using spectral indices. Considering the non-linearity of the relationship between LAI and spectral reflectance, linear multivariate regression showed almost satisfactory results, and in the best conditions, this relationship has a correlation coefficient of 75.46 and 72.91, with an error of 0.229 and 0.308, respectively, for the Landsat 9 and Sentinel 2. LAI estimation using machine learning techniques is suitable and very capable for observing LAI developments and increases the accuracy of calculations.

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Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model

Cited by 14 publications

References 51 publications

Image-Based Phenotyping for Non-Destructive In Situ Rice (Oryza sativa L.) Tiller Counting Using Proximal Sensing

Image-Based Phenotyping for Non-Destructive In Situ Rice (Oryza sativa L.) Tiller Counting Using Proximal Sensing

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

Evaluation of Landsat 9 and Sentinel 2 satellites in estimation of cotton leaf area index using M5 tree model

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