Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1-V2 stage) and at the V6-V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs.

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Optical sensing estimation of leaf nitrogen concentration in maize across a range of water-stress levels

Crop and Pasture Science ◽

10.1071/cp10374 ◽

2011 ◽

Vol 62 (6) ◽

pp. 474 ◽

Cited By ~ 9

Author(s):

Tong-Chao Wang ◽

B. L. Ma ◽

You-Cai Xiong ◽

M. Farrukh Saleem ◽

Feng-Min Li

Keyword(s):

Water Stress ◽

Vegetation Index ◽

Optical Sensing ◽

Leaf Nitrogen ◽

Growth Stages ◽

N Concentration ◽

Leaf N Concentration ◽

Chlorophyll Index ◽

Plant Moisture ◽

Leaf N

Optical sensing techniques offer an instant estimation of leaf nitrogen (N) concentration during the crop growing season. Differences in plant-moisture status, however, can obscure the detection of differences in N levels. This study presents a vegetation index that robustly measures differences in foliar N levels across a range of plant moisture levels. A controlled glasshouse study with maize (Zea mays L.) subjected to both water and N regimes was conducted in Ottawa, Canada. The purpose of the study was to identify spectral waveband(s), or indices derived from different wavebands, such as the normalised difference vegetation index (NDVI), that are capable of detecting variations in leaf N concentration in response to different water and N stresses. The experimental design includes three N rates and three water regimes in a factorial arrangement. Leaf chlorophyll content and spectral reflectance (400–1075 nm) were measured on the uppermost fully expanded leaves at the V6, V9 and V12 growth stages (6th, 9th and 12th leaves fully expanded). N concentrations of the same leaves were determined using destructive sampling. A quantitative relationship between leaf N concentration and the normalised chlorophyll index (normalised to well fertilised and well irrigated plants) was established. Leaf N concentration was also a linear function (R2 = 0.9, P < 0.01) of reflectance index (NDVI550, 760) at the V9 and V12 growth stages. Chlorophyll index increased with N nutrition, but decreased with water stress. Leaf reflectance at wavebands of 550 ± 5 nm and 760 ± 5 nm were able to separate water- and N-stressed plants from normal growing plants with sufficient water and N supply. Our results suggest that NDVI550, 760 and normalised chlorophyll index hold promise for the assessment of leaf N concentration at the leaf level of both normal and water-stressed maize plants.

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Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages

Remote Sensing ◽

10.3390/rs12071139 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1139

Author(s):

Rui Dong ◽

Yuxin Miao ◽

Xinbing Wang ◽

Zhichao Chen ◽

Fei Yuan ◽

...

Keyword(s):

Precision Agriculture ◽

Fluorescence Sensor ◽

Accurate Estimation ◽

Growth Stages ◽

Fluorescence Sensors ◽

N Application ◽

N Concentration ◽

Leaf N Concentration ◽

N Status ◽

Leaf N

Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R2 = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.

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Effect of spad techniques and planting density on 'Y' leaf nitrogen concentration in transplanted rice

Acta Agronomica Hungarica ◽

10.1556/aagr.52.2004.1.11 ◽

2004 ◽

Vol 52 (1) ◽

pp. 95-104 ◽

Cited By ~ 1

Author(s):

P. Janaki ◽

T. M. Thiyagarajan

Keyword(s):

Grain Yield ◽

Field Experiments ◽

Nitrogen Concentration ◽

Planting Density ◽

Growth Stages ◽

Chlorophyll Meter ◽

N Concentration ◽

Leaf N Concentration ◽

N Management ◽

Leaf N

Field experiments were conducted in June-September 1998 and 1999 with rice variety ASD18 at the wetland farm of Tamil Nadu Agricultural University, in Coimbatore, India to examine variations in 'Y' leaf (youngest fully expanded leaf) N concentration as influenced by different planting densities and N management strategies in a split plot design. The main plot consisted of three plant populations (33, 66 and 100 hills m-2) and the sub-plots treatments of five N management approaches. The results revealed that the nitrogen concentration progressively declined with growth, the decline being steep up to 35 days after transplanting, wereafter the values became almost linear up to the flowering stage in all the treatments. The mean 'Y' leaf N was found to be significantly higher at 33 hills m-2 (45.1 g kg-1), while the other two densities were on par (42.9 g kg-1). When N application was based on chlorophyll meter (SPAD) values the leaf N concentration was maintained at a level of 39.2 to 51.9 g kg-1 to produce maximum grain yield. A significant correlation was observed between the chlorophyll meter values and 'Y' leaf N concentrations at various days after transplanting (r values ranged from 0.57* to 0.83**), while the correlation was highly significant during the major physiological growth stages. Though the 'Y' leaf content was significantly higher in the treatment involving Sesbania rostrata green manuring + 150 kg N applied in splits, the grain yield produced was on par in all the N applied treatments. A highly significant correlation was observed between the grain yield and both 'Y' leaf N content and SPAD values during various growth periods.

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Investigating a Selection of Methods for the Prediction of Total Soluble Solids Among Wine Grape Quality Characteristics Using Normalized Difference Vegetation Index Data From Proximal and Remote Sensing

Frontiers in Plant Science ◽

10.3389/fpls.2021.683078 ◽

2021 ◽

Vol 12 ◽

Author(s):

Aikaterini Kasimati ◽

Borja Espejo-Garcia ◽

Eleanna Vali ◽

Ioannis Malounas ◽

Spyros Fountas

Keyword(s):

Machine Learning ◽

Regression Models ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Quality Characteristics ◽

Growth Stages ◽

Wine Grape ◽

Remote Sensors ◽

Grape Quality ◽

Different Growth Stages

The most common method for determining wine grape quality characteristics is to perform sample-based laboratory analysis, which can be time-consuming and expensive. In this article, we investigate an alternative approach to predict wine grape quality characteristics by combining machine learning techniques and normalized difference vegetation index (NDVI) data collected at different growth stages with non-destructive methods, such as proximal and remote sensing, that are currently used in precision viticulture (PV). The study involved several sets of high-resolution multispectral data derived from four sources, including two vehicle-mounted crop reflectance sensors, unmanned aerial vehicle (UAV)-acquired data, and Sentinel-2 (S2) archived imagery to estimate grapevine canopy properties at different growth stages. Several data pre-processing techniques were employed, including data quality assessment, data interpolation onto a 100-cell grid (10 × 20 m), and data normalization. By calculating Pearson’s correlation matrix between all variables, initial descriptive statistical analysis was carried out to investigate the relationships between NDVI data from all proximal and remote sensors and the grape quality characteristics in all growth stages. The transformed dataset was then ready and applied to statistical and machine learning algorithms, firstly trained on the data distribution available and then validated and tested, using linear and nonlinear regression models, including ordinary least square (OLS), Theil–Sen, and the Huber regression models and Ensemble Methods based on Decision Trees. Proximal sensors performed better in wine grapes quality parameters prediction in the early season, while remote sensors during later growth stages. The strongest correlations with the sugar content were observed for NDVI data collected with the UAV, Spectrosense+GPS (SS), and the CropCircle (CC), during Berries pea-sized and the Veraison stage, mid-late season with full canopy growth, for both years. UAV and SS data proved to be more accurate in predicting the sugars out of all wine grape quality characteristics, especially during a mid-late season with full canopy growth, in Berries pea-sized and the Veraison growth stages. The best-fitted regressions presented a maximum coefficient of determination (R2) of 0.61.

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LIMITES CRÍTICOS DE NDVI PARA ESTIMATIVA DO POTENCIAL PRODUTIVO DO MILHO

Revista Brasileira de Milho e Sorgo ◽

10.18512/1980-6477/rbms.v17n1p91-100 ◽

2018 ◽

Vol 17 (1) ◽

pp. 91 ◽

Cited By ~ 3

Author(s):

ANDRÉ LUIS VIAN ◽

CHRISTIAN BREDEMEIER ◽

PAULO REGIS FERREIRA DA SILVA ◽

ANTÔNIO LUIS SANTI ◽

CECÍLIA PAZ GIORDANO DA SILVA ◽

...

Keyword(s):

Zea Mays ◽

Grain Yield ◽

Avena Sativa ◽

Vegetation Index ◽

Agricultural Practices ◽

Growth Cycle ◽

Yield Potential ◽

Growth Stages ◽

Different Growth Stages ◽

Critical Limits

RESUMO - A estimativa do potencial produtivo da cultura do milho ao longo do ciclo de desenvolvimento é uma das novas práticas agrícolas que vêm sendo utilizadas para qualificar o manejo da cultura. Neste sentido, destaca-se a inserção de sensores de vegetação, com a finalidade de realizar o monitoramento do desenvolvimento e da condição nutricional da cultura ao longo do seu ciclo. O objetivo do presente trabalho foi determinar os limites críticos do Índice de vegetação por diferença normalizada (NDVI) para a determinação de classes de potencial produtivo do milho em diferentes estádios fenológicos, utilizando sensor óptico ativo de vegetação (Greenseeker). O experimento foi conduzido na EEA/UFRGS, durante a safra agrícola 2013/2014. Os tratamentos consistiram de diferentes épocas de dessecação da aveia branca (Avena sativa L.) antes da semeadura da cultura do milho. As avaliações com o sensor óptico ativo foram realizadas nos estádios fenológicos V3, V5, V6, V7 e V8. Os resultados mostraram que o NDVI medido pelo sensor Greenseeker foi eficiente na predição da produtividade de milho em diferentes estádios fenológicos. Os limites críticos de NDVI, os quais correspondem a diferentes classes de potencial produtivo, podem ser identificados de maneira rápida e precisa entre os estádios fenológicos V3 a V8 e esta informação pode ser empregada para a adubação nitrogenada em taxa variável de acordo com o potencial produtivo estimado. Palavras-chave: Greenseeker, índice de vegetação, Zea mays, NDVI. CRITICAL LIMITS OF NDVI FOR YIELD POTENTIAL ESTIMATION IN MAIZE ABSTRACT - The estimation of grain yield potential of maize along the growth cycle is one of new agricultural practices that have been used to qualify crop management. In this sense, the use of vegetation sensors can be highlighted, in order to carry out the monitoring of plant development and nutritional condition throughout its development. The objective of this study was to indicate NDVI critical limits for determining grain yield potential classes of maize in different growth stages using an active optical vegetation sensor (Greenseeker). The experiment was carried out in the 2013/14 growing season, in Eldorado do Sul, State of Rio Grande do Sul, southern Brazil. Treatments consisted of different dissecation timing of oat (Avena sativa L.) before corn sowing. Evaluations with the active optical sensor were done at growth stages V3, V5, V6, V7, and V8. Results showed that NDVI measured by the sensor Greenseeker was efficient in identifying maize grain productivity at different growth stages. NDVI critical limits that correspond to different yield potential levels in maize can be quickly and precisely identified between V3 and V8 growth stages and this information can be used for site-specific nitrogen fertilization according to the estimated yield potential. Keywords: Greenseeker, vegetation index, Zea mays, NDVI.

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Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages

Remote Sensing ◽

10.3390/rs11040387 ◽

2019 ◽

Vol 11 (4) ◽

pp. 387 ◽

Cited By ~ 18

Author(s):

Ke Zhang ◽

Xiaokang Ge ◽

Pengcheng Shen ◽

Wanyu Li ◽

Xiaojun Liu ◽

...

Keyword(s):

Grain Yield ◽

Real Time ◽

Dynamic Model ◽

Vegetation Index ◽

N Fertilizer ◽

Growth Stages ◽

Rice Grain ◽

N Nutrition ◽

Red Edge ◽

Vegetation Indexes

Predicting the grain yield during early to mid-growth stages is important for initial diagnosis of rice and quantitative regulation of topdressing. In this study, we conducted four experiments using different nitrogen (N) application rates (0–400 kg N∙ha−1) in three Japonica rice cultivars (Wuyunjing24, Ningjing4, and Lianjing7) grown in Jiangsu province, Eastern China, from 2015–2016. Spectral reflectance data were collected multiple times during early to mid-growth stages using an active mounted sensor (RapidScan CS-45, Holland Scientific Inc., Lincoln, NE, USA). Data were then used to calculate optimal vegetation indexes (normalized difference red edge, NDRE; normalized difference vegetation index, NDVI; ratio vegetation index, RVI; red-edge ratio vegetation index, RERVI), which were used to develop a dynamic change model and in-season grain yield prediction model. The NDRE index was more stable than other indexes (NDVI, RVI, RERVI), showing less standard deviation at the same N fertilizer rate. The R2 of the relationships between leaf area index (LAI), plant nitrogen accumulation (PNA), and NDRE also increased compared to other indexes. These findings suggest that NDRE is suitable for analysis of paddy rice N nutrition. According to real-time series changes in NDRE, the resulting dynamic model followed a sigmoid curve, with a coefficient of determination (R2) >0.9 and relative root-mean-square error <5%. Moreover, the feature platform value (saturation value, SV) of the NDRE-based model accurately predicted the differences between treatments and the final grain yield levels. R2 values of the relationship between SV and yield were >0.7. For every 0.1 increase in SV, grain yield increased by 3608.1 kg·ha−1. Overall, our new dynamic model effectively predicted grain yield at stem elongation and booting stages, providing real-time crop N nutrition data for management of N fertilizer topdressing in rice production.

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Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System

Remote Sensing ◽

10.3390/rs13030401 ◽

2021 ◽

Vol 13 (3) ◽

pp. 401

Author(s):

Cadan Cummings ◽

Yuxin Miao ◽

Gabriel Dias Paiao ◽

Shujiang Kang ◽

Fabián G. Fernández

Keyword(s):

Machine Learning ◽

Chlorophyll Content ◽

Vegetation Index ◽

Soil Drainage ◽

Management Information ◽

Area Index ◽

Sensing System ◽

Extreme Gradient Boosting ◽

Split Plot ◽

N Status

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

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Prediction of Maize Yield at the City Level in China Using Multi-Source Data

Remote Sensing ◽

10.3390/rs13010146 ◽

2021 ◽

Vol 13 (1) ◽

pp. 146

Author(s):

Xinxin Chen ◽

Lan Feng ◽

Rui Yao ◽

Xiaojun Wu ◽

Jia Sun ◽

...

Keyword(s):

Machine Learning ◽

Satellite Data ◽

Vegetation Index ◽

Maize Yield ◽

Machine Learning Algorithms ◽

Shortwave Radiation ◽

Growth Stages ◽

Climate Data ◽

Early Peak ◽

The City

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

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Improving Biomass and Grain Yield Prediction of Wheat GenoTypes on Sodic Soil Using Integrated High-Resolution MultiSpectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Remote Sensing ◽

10.3390/rs13173482 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3482

Author(s):

Malini Roy Choudhury ◽

Sumanta Das ◽

Jack Christopher ◽

Armando Apan ◽

Scott Chapman ◽

...

Keyword(s):

Machine Learning ◽

Grain Yield ◽

Point Cloud ◽

Vegetation Index ◽

3D Point Cloud ◽

Sodic Soil ◽

Proximal Sensing ◽

Crop Height ◽

Wheat Genotypes ◽

Grain Yields

Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to assist crop phenotyping on sodic soil are limited. Hence, this study evaluated the ability of multispectral, hyperspectral, 3D point cloud, and machine learning techniques to improve estimation of biomass and grain yield of wheat genotypes grown on a moderately sodic (MS) and highly sodic (HS) soil sites in northeastern Australia. While a number of studies have reported using different remote sensing approaches and crop traits to quantify crop growth, stress, and yield variation, studies are limited using the combination of these techniques including machine learning to improve estimation of genotypic biomass and yield, especially in constrained sodic soil environments. At close to flowering, unmanned aerial vehicle (UAV) and ground-based proximal sensing was used to obtain remote and/or proximal sensing data, while biomass yield and crop heights were also manually measured in the field. Grain yield was machine-harvested at maturity. UAV remote and/or proximal sensing-derived spectral vegetation indices (VIs), such as normalized difference vegetation index, optimized soil adjusted vegetation index, and enhanced vegetation index and crop height were closely corresponded to wheat genotypic biomass and grain yields. UAV multispectral VIs more closely associated with biomass and grain yields compared to proximal sensing data. The red-green-blue (RGB) 3D point cloud technique was effective in determining crop height, which was slightly better correlated with genotypic biomass and grain yield than ground-measured crop height data. These remote sensing-derived crop traits (VIs and crop height) and wheat biomass and grain yields were further simulated using machine learning algorithms (multitarget linear regression, support vector machine regression, Gaussian process regression, and artificial neural network) with different kernels to improve estimation of biomass and grain yield. The artificial neural network predicted biomass yield (R2 = 0.89; RMSE = 34.8 g/m2 for the MS and R2 = 0.82; RMSE = 26.4 g/m2 for the HS site) and grain yield (R2 = 0.88; RMSE = 11.8 g/m2 for the MS and R2 = 0.74; RMSE = 16.1 g/m2 for the HS site) with slightly less error than the others. Wheat genotypes Mitch, Corack, Mace, Trojan, Lancer, and Bremer were identified as more tolerant to sodic soil constraints than Emu Rock, Janz, Flanker, and Gladius. The study improves our ability to select appropriate traits and techniques in accurate estimation of wheat genotypic biomass and grain yields on sodic soils. This will also assist farmers in identifying cultivars tolerant to sodic soil constraints.

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Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

Remote Sensing ◽

10.3390/rs12223684 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3684

Author(s):

Jie Jiang ◽

Zeyu Zhang ◽

Qiang Cao ◽

Yan Liang ◽

Brian Krienke ◽

...

Keyword(s):

Winter Wheat ◽

Dry Matter ◽

Vegetation Index ◽

Nutrition Status ◽

Growth Stages ◽

Growth Indices ◽

N Accumulation ◽

N Nutrition ◽

Red Edge ◽

Aerial Vehicle

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.

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