Selection of Hyperspectral Vegetation Indices for Monitoring Yield and Physiological Response in Sweet Maize under Different Water and Nitrogen Availability

Sellami, Mohamed; Albrizio, Rossella; Colovic, Milica; Hamze, Mohamad; Cantore, V.; Todorović, Mladen; Piscitelli, Lea

doi:10.3390/agronomy12020489

Cited by 10 publications

(4 citation statements)

References 107 publications

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“…The sensitive spectral indices of this study were ρ nir , H 500nm , H 675nm , F 710nm , λ 710nm , S 730nm , and λ 730nm due to the influence of salinized soil. Hyperspectral estimation of physiological parameters of sweet corn and other crops showed that the chlorophyll absorption reflectance index (CARI), double difference index (DD), red edge inflection point (REIP), and chlorophyll red edge (CIred-edge) were good predictors of the physiological parameters, confirming the key role of the red edge spectral region [41]. Compared with the above study, the spectral index was not constructed in this study.…”

Section: Effects Of the Nitrogen Application Rate On The Spectral Cha...mentioning

confidence: 62%

Analysis of Spectral Characteristics of Cotton Leaves at Bud Stage under Different Nitrogen Application Rates

Wang,

Yin,

Liu

et al. 2024

Agronomy

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Soil salinity affects nutrient uptake by cotton. The cotton bud stage is a very important period in the process of cotton planting and directly affects the yield of cotton. The nutritional status of the bud stage directly affects the reflectance spectra of cotton canopy leaves. Therefore, it is of great significance to nondestructively monitor the nutritional status of the cotton bud stage on salinized soil via spectroscopic techniques and perform corresponding management measures to improve cotton yield. In this study, potted plants with different nitrogen application rates were set up to obtain the reflection spectral curves of cotton bud stage leaves, analyze their spectral characteristics under different nitrogen application rates, and establish spectral estimation models of chlorophyll density. The results are as follows: in the continuum removal spectrum of the cotton bud stage, the lowest point of the absorption valley near 500 nm shifted to the shortwave direction with an increasing nitrogen application rate. The mean reflectance between 765 and 880 nm was significantly different between nitrogen-stressed and nitrogen-unstressed cotton. The average reflectance of the near-infrared band, the absorption valley depths near 500 nm and 675 nm, the first derivative of the 710 nm reflectance, and the second derivatives of the 690 nm and 730 nm reflectance increased with increasing nitrogen application and chlorophyll density, and significant correlations were observed with the chlorophyll density. These parameters were modeled using support vector regression (SVR) and artificial neural network (ANN) methods, two commonly used algorithms in the field of machine learning. The determination coefficients of the three chlorophyll samples via the ANN models were 0.92, 0.77, and 0.94 for the modeling set and 0.77, 0.69, and 0.77 for the verification set. The ratio of quartile to root-mean-square error (RPIQ) of the ANN model was greater than 2.2, and the ratio of the standard error of the measured value to the standard error of the predicted (SEL/SEP) was close to 1, indicating that the chlorophyll density estimation models built based on the ANN algorithm had robust prediction ability. Our model could accurately estimate the leaf chlorophyll density in the cotton bud stage.

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Section: Effects Of the Nitrogen Application Rate On The Spectral Cha...mentioning

confidence: 62%

Analysis of Spectral Characteristics of Cotton Leaves at Bud Stage under Different Nitrogen Application Rates

Wang,

Yin,

Liu

et al. 2024

Agronomy

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“…It has also been employed for the estimation of characteristics such as canopy coverage, leaf area index and leaf chlorophyll content [39]. The TCARI index has been used to monitor the yield and physiological response of sweet corn [40] because it is sensitive to leaf chlorophyll content [41]. It has also been found that it can be used to estimate plant nutrition with model variance values up to 0.83% [42].…”

Section: Discussionmentioning

confidence: 99%

Spectral Index-Based Estimation of Total Nitrogen in Forage Maize: A Comparative Analysis of Machine Learning Algorithms

Martínez-Sifuentes,

Trucíos-Caciano,

López-Hernández

et al. 2024

Nitrogen

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Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine the efficient use of nitrogen fertilization in a context of sustainable agronomic management by avoiding Nitrogen loss and preventing it from becoming a pollutant for the soil and the atmosphere. The combination of machine learning algorithms with vegetation spectral indices is a new practice that helps estimate parameters of agricultural importance such as nitrogen. The objective of the present study was to compare random forest and neural network algorithms for estimating total plant nitrogen with spectral indices. Five spectral indices were obtained from remotely piloted aircraft systems and analyzed by mean, maximum and minimum from each sample plot to finally obtain 15 indices, and total nitrogen was estimated from the georeferenced points. The most important variables were selected with backward, forward and stepwise methods and total nitrogen estimates by laboratory were compared with random forest models and artificial neural networks. The most important indices were NDREmax and TCARImax. Using 15 spectral indices, total nitrogen with a variance of 79% and 81% with random forest and artificial neural network, respectively, was estimated. And only using NDREmax and TCARmax indices, 73% and 79% were explained by random forest and artificial neural network, respectively. It is concluded that it is possible to estimate nitrogen in forage maize with two indices and it is recommended to analyze by phenological stage and with a greater number of field data.

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“…DD, REIP, and CI red-edge , belonging to the red-edge group VIs, were the best predictors of physiological and yield parameters at the mid-season stage of sweet maize crop. The differences among water supplies and nitrogen treatments are mainly related to chlorophyll content [66].…”

Section: Rgb and Hyperspectral Vegetation Indices For Estimating Maiz...mentioning

confidence: 99%

Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize

Colovic,

Stellacci,

Mzid

et al. 2024

Agronomy

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This study analyzed the capability of aerial RGB (red-green-blue) and hyperspectral-derived vegetation indices to assess the response of sweet maize (Zea mays var. saccharata L.) to different water and nitrogen inputs. A field experiment was carried out during 2020 by using both remote RGB images and ground hyperspectral sensor data. Physiological parameters (i.e., leaf area index, relative water content, leaf chlorophyll content index, and gas exchange parameters) were measured. Correlation and multivariate data analysis (principal component analysis and stepwise linear regression) were performed to assess the strength of the relationships between eco-physiological measured variables and both RGB indices and hyperspectral data. The results revealed that the red-edge indices including CIred-edge, NDRE and DD were the best predictors of the maize physiological traits. In addition, stepwise linear regression highlighted the importance of both WI and WI:NDVI for prediction of relative water content and crop temperature. Among the RGB indices, the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content. Moreover, the coefficients of correlation between studied crop variables and GGA, NDLuv and NDLab were higher than with the hyperspectral indices measured at the ground level. The findings confirmed the capacity of selected RGB and hyperspectral indices to evaluate the water and nitrogen status of sweet maize and provided opportunity to expand experimentation on other crops, diverse pedo-climatic conditions and management practices. Hence, the aerially collected RGB vegetation indices might represent a cost-effective solution for crop status assessment.

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Selection of Hyperspectral Vegetation Indices for Monitoring Yield and Physiological Response in Sweet Maize under Different Water and Nitrogen Availability

Cited by 10 publications

References 107 publications

Analysis of Spectral Characteristics of Cotton Leaves at Bud Stage under Different Nitrogen Application Rates

Analysis of Spectral Characteristics of Cotton Leaves at Bud Stage under Different Nitrogen Application Rates

Spectral Index-Based Estimation of Total Nitrogen in Forage Maize: A Comparative Analysis of Machine Learning Algorithms

Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize

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