Random forest regression results in accurate assessment of potato nitrogen status based on multispectral data from different platforms and the critical concentration approach

Peng, Junxiang; Manevski, Kiril; Kørup, Kirsten; Larsen, Ryan Godsk; Andersen, Mathias Neumann

doi:10.1016/j.fcr.2021.108158

Cited by 41 publications

(21 citation statements)

References 69 publications

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“…Hyperspectral features, such as reflectance of sensitive band, "three edge" parameters, and vegetation indices (VIs) were used to identify sensitive regions to specific crop parameters [25]. Numerous studies showed the feasibility of using hyperspectral remote sensing for real-time monitoring of crop N nutrition status [26], such as leaf chlorophyll content (LCC) [27], LNC [28], leaf nitrogen accumulation (LNA) [29], plant nitrogen concentration (PNC) [30], plant nitrogen uptake (PNU) [31], nitrogen nutrition index (NNI) [32], etc. VIs by comprehensive analyses on canopy spectral reflectance from visible to near-infrared light were regarded as the most important hyperspectral features for monitoring crop N content.…”

Section: Introductionmentioning

confidence: 99%

Nitrogen Balance Index Prediction of Winter Wheat by Canopy Hyperspectral Transformation and Machine Learning

Fan

Chen

et al. 2022

Remote Sensing

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Nitrogen balance index (NBI) is an important indicator for scientific diagnostic and quantitative research on crop growth status. The quick and accurate assessment of NBI is necessary for farmers to make timely N management decisions. The objective of the study was to estimate winter wheat NBI based on canopy hyperspectral features between 400–1350 nm combined with machine learning (ML) methods in the individual and whole growth stages. In this study, 3 years of winter wheat plot experiments were conducted. Ground-level canopy hyperspectral reflectance and corresponding plant NBI values were measured during the jointing, booting, flowering and filling stages. Continuous removal spectra (CRS) and logarithmic transformation spectra (LOGS) were derived from the original canopy spectra. Sensitive bands and vegetation indices (VIs) highly correlated with NBI under different spectral transformations were selected as hyperspectral features to construct the NBI estimation models combined with ML algorithms. The study indicated that the spectral transformation significantly improved the correlation between the sensitive bands, VIs and the NBI. The correlation coefficient of the sensitive band in CRS in the booting stage increased by 27.87%, reaching −0.78. The leaf chlorophyll index (LCI) in LOGS had the highest correlation with NBI in the filling stage, reaching a correlation coefficient of −0.96. The NBI prediction accuracies based on the sensitive band combined with VIs were generally better than those based on the univariate hyperspectral feature, and the prediction accuracy of each growth stage was better than that of the whole growth stage. The random forest regression (RFR) method performed better than the support vector regression (SVR) and partial least squares regression (PLS) methods. The NBI estimation model based on the LOGS-RFR method in the filling stage could explain 95% of the NBI variability with relative prediction deviation (RPD) being 3.69. These results will provide a scientific basis for better nitrogen nutrition monitoring, diagnosis, and later for field management of winter wheat.

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Section: Introductionmentioning

confidence: 99%

Nitrogen Balance Index Prediction of Winter Wheat by Canopy Hyperspectral Transformation and Machine Learning

Fan

Chen

et al. 2022

Remote Sensing

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“…When establishing models for different crops, various spectral features including spectral reflectance, existed vegetation indices (VIs), and newly proposed VIs can be used as input variables. Taking N prediction as an example, Peng et al used the ratio vegetation index (RVI), the normalized difference red-edge index (NDRE) and the terrestrial chlorophyll index (TCI), to predict potato N status [ 27 ]. Zhang et al used RVI and the normalized difference vegetation index (NDVI) to predict rice N status [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Nong

et al. 2022

Sensors

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Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.

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“…In recent years, RFR has been used widely in applications related to soil-crop sensing methods [43][44][45]. The RFR is an ensemble learning technique developed by Breiman [46] that involves combining a large set of decision trees generated independently so that no two trees are the same.…”

Section: Estimation Of Soil Properties With Random Forest Regression ...mentioning

confidence: 99%

Assessment of Soil Fertility Using Induced Fluorescence and Machine Learning

Longchamps

Mandal

Khosla

2022

Sensors

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Techniques such as proximal soil sampling are investigated to increase the sampling density and hence the resolution at which nutrient prescription maps are developed. With the advent of a commercial mobile fluorescence sensor, this study assessed the potential of fluorescence to estimate soil chemical properties and fertilizer recommendations. This experiment was conducted over two years at nine sites on 168 soil samples and used random forest regression to estimate soil properties, fertility classes, and recommended N rates for maize production based on induced fluorescence of air-dried soil samples. Results showed that important soil properties such as soil organic matter, pH, and CEC can be estimated with a correlation of 0.74, 0.75, and 0.75, respectively. When attempting to predict fertility classes, this approach yielded an overall accuracy of 0.54, 0.78, and 0.69 for NO3-N, SOM, and Zn, respectively. The N rate recommendation for maize can be directly estimated by fluorescence readings of the soil with an overall accuracy of 0.78. These results suggest that induced fluorescence is a viable approach for assessing soil fertility. More research is required to transpose these laboratory-acquired soil analysis results to in situ readings successfully.

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Random forest regression results in accurate assessment of potato nitrogen status based on multispectral data from different platforms and the critical concentration approach

Cited by 41 publications

References 69 publications

Nitrogen Balance Index Prediction of Winter Wheat by Canopy Hyperspectral Transformation and Machine Learning

Nitrogen Balance Index Prediction of Winter Wheat by Canopy Hyperspectral Transformation and Machine Learning

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Assessment of Soil Fertility Using Induced Fluorescence and Machine Learning

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