Combining fixed-wing UAV multispectral imagery and machine learning to diagnose winter wheat nitrogen status at the farm scale

Jiang, Jie; Atkinson, Peter M.; Zhang, Jiayi; Lu, Ruhua; Zhou, Youyan; Cao, Qiang; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Liu, Xiaojun

doi:10.1016/j.eja.2022.126537

Cited by 24 publications

(10 citation statements)

References 49 publications

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“…The results showed that 1 the impact of image features on the prediction model of wheat plant nitrogen content was little when the image spatial resolution varied from 0.01, 0.02 to 0.05 m. The R 2 of the plant nitrogen content models constructed with three resolutions were 0.84, 0.86, and 0.92, respectively. 2 The model constructed with different resolution features showed different prediction accuracy but a reasonably good transferability when applied to coarser (upscaling) or finer (downscaling) images. 3 The effects of image resolutions on model performance and transferability are because image resolutions mostly affect texture features rather than spectral features.…”

Section: Discussionmentioning

confidence: 91%

“…Various VIs have been widely used for qualitative and quantitative evaluation of vegetation cover and crop growth dynamics [36][37][38]. Based on the relevant literature [2,[17][18][19][20][21][22][23][24][25][39][40][41][42][43][44][45][46], nine commonly used nitrogen content prediction vegetation indices were selected in this study with the formula shown in Table 2.…”

Section: Vegetation Indices Selectionmentioning

confidence: 99%

“…Nitrogen regulates the growth of wheat at different growth stages and has a significant impact on wheat yield and quality [1,2] and thus, timely and accurate monitoring of nitrogen is important to guide variable fertilization [3,4]. When wheat plants are nitrogen deficient, both external morphology and internal metabolism differ from normal plants, and therefore the nitrogen nutrition status can be monitored and diagnosed by easily accessible phenological information [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown in various studies that ratio indices and/or normalized difference indices perform very well in estimating nitrogen content. For example, the Excessive Green Index (EXG), the modified chlorophyll absorption in reflectance index (MCARI), the Normalized Difference Vegetation Index (NDVI), the Normalized Green-Red Difference Index (NGRDI), the Soil Adjusted Vegetation Index (SAVI), the Transformed Chlorophyll Absorption Ratio Index (TCARI), The Rededge Soil Adjusted Vegetation Index (RESAVI), the Renormalized Difference Vegetation Index for Rededge (RERDVI), and the Ratio Vegetation Index (RVI) have been proposed for estimating canopy nitrogen content and indicated good estimation accuracy [2,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

Guo

Huang

et al. 2022

Drones

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UAV imaging provides an efficient and non-destructive tool for characterizing farm information, but the quality of the UAV model is often affected by the image’s spatial resolution. In this paper, the predictability of models established using UAV multispectral images with different spatial resolutions for nitrogen content of winter wheat was evaluated during the critical growth stages of winter wheat over the period 2021–2022. Feature selection based on UAV image reflectance, vegetation indices, and texture was conducted using the competitive adaptive reweighted sampling, and the random forest machine learning method was used to construct the prediction model with the optimized features. Results showed that model performance increased with decreasing image spatial resolution with a R2, a RMSE, a MAE and a RPD of 0.84, 4.57 g m−2, 2.50 g m−2 and 2.34 (0.01 m spatial resolution image), 0.86, 4.15 g m−2, 2.82 g m−2 and 2.65 (0.02 m), and 0.92, 3.17 g m−2, 2.45 g m−2 and 2.86 (0.05 m), respectively. Further, the transferability of models differed when applied to images with coarser (upscaling) or finer (downscaling) resolutions. For upscaling, the model established with the 0.01 m images had a R2 of 0.84 and 0.89 when applied to images with 0.02 m and 0.05 m resolutions, respectively. For downscaling, the model established with the 0.05 m image features had a R2 of 0.86 and 0.83 when applied to images of 0.01 m and 0.02 m resolutions. Though the image spatial resolution affects image texture features more than the spectral features and the effects of image spatial resolution on model performance and transferability decrease with increasing plant wetness under irrigation treatment, it can be concluded that all the UAV images acquired in this study with different resolutions could achieve good predictions and transferability of the nitrogen content of winter wheat plants.

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Section: Discussionmentioning

confidence: 91%

Section: Vegetation Indices Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

Guo

Huang

et al. 2022

Drones

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“…The NNI can be interpreted as follows: values above 1 indicate "excessive" nitrogen luxury consumption, values close to 1 "satisfactory" and below 1 "deficiency" (Lemaire et al, 2008). NNI has been studied in many ecosystems (Gastal et al, 2014) and demonstrated to be an efficient and consistent estimate of the nitrogen nutritional status of plants across multiple crops, climates, and soil conditions, and is often used as calibration method for nitrogen status diagnostic tools (Errecart et al, 2012;Chen et al, 2015;Liu et al, 2018;Zha et al, 2020;Louarn et al, 2021;Jiang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Association of tropical forage grasses in pastures: agronomic aspects that determine plant performance

Silva¹

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for their constant encouragement and understanding. Without your love and support, I would have never made it. This thesis is dedicated to you.To my advisor, Dr. Sila Carneiro da Silva, for his guidance and support during my doctorate program. Above the scientific insights you provided me, thank you for the example of professor and researcher, conducting all activities with professionalism, dedication, and commitment.Thanks are also due to the University of São Paulo, "Luiz de Queiroz" College of Agriculture, and the faculty staff of the Department of Animal Science that gave me the opportunity and contributed to my formation.

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Coupling continuous wavelet transform with machine learning to improve water status prediction in winter wheat

Zhuang¹,

Zhang²,

Li³

et al. 2023

Precision Agric

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Combining fixed-wing UAV multispectral imagery and machine learning to diagnose winter wheat nitrogen status at the farm scale

Cited by 24 publications

References 49 publications

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

Association of tropical forage grasses in pastures: agronomic aspects that determine plant performance

Coupling continuous wavelet transform with machine learning to improve water status prediction in winter wheat

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