Reflectance spectrometry applied to the analysis of nitrogen and potassium deficiency in cotton

Oliveira, Marcos Renato de; Queiroz, Thales Rafael Guimarães; Teixeira, Adunias dos Santos; Moreira, Luís Clênio Jário; Leão, Raimundo Alípio de Oliveira

doi:10.5935/1806-6690.20200074

Cited by 4 publications

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“…Research has indicated that the spectral characteristics of plant canopy reflections are closely related to several biochemical and biophysical properties, such as pigment concentrations [ 31 , 32 ], plant vigor [ 33 , 34 ], water status [ 35 , 36 ], and nutritional status [ 37 , 38 ]. Different nitrogen states in cotton cause reflectance changes in multiple bands of the spectrum, such as 550–700 nm [ 39 ], 705–715 nm [ 40 ], and 1325–1575 nm [ 41 ]. Hyperspectral remote sensing plays an important role in plant nitrogen nutrition monitoring as a cutting–edge technology.…”

Section: Introductionmentioning

confidence: 99%

Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Gossypium hirsutum L.) under Different Nitrogen Fertilizer Application Based on Machine Learning Algorithms

Han

Zhai

Liu

et al. 2023

Plants

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Hyperspectral technology has enabled rapid and efficient nitrogen monitoring in crops. However, most approaches involve direct monitoring of nitrogen content or physiological and biochemical indicators directly related to nitrogen, which cannot reflect the overall plant nutritional status. Two important photosynthetic traits, the fraction of absorbed photosynthetically active radiation (FAPAR) and the net photosynthetic rate (Pn), were previously shown to respond positively to nitrogen changes. Here, Pn and FAPAR were used for correlation analysis with hyperspectral data to establish a relationship between nitrogen status and hyperspectral characteristics through photosynthetic traits. Using principal component and band autocorrelation analyses of the original spectral reflectance, two band positions (350–450 and 600–750 nm) sensitive to nitrogen changes were obtained. The performances of four machine learning algorithm models based on six forms of hyperspectral transformations showed that the light gradient boosting machine (LightGBM) model based on the hyperspectral first derivative could better invert the Pn of function–leaves in cotton, and the random forest (RF) model based on hyperspectral first derivative could better invert the FAPAR of the cotton canopy. These results provide advanced metrics for non–destructive tracking of cotton nitrogen status, which can be used to diagnose nitrogen nutrition and cotton growth status in large farms.

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Section: Introductionmentioning

confidence: 99%

Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Gossypium hirsutum L.) under Different Nitrogen Fertilizer Application Based on Machine Learning Algorithms

Han

Zhai

Liu

et al. 2023

Plants

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“…On the other hand, hyperspectral imaging can simultaneously obtain the target spectrum and image information, and is regarded as a technique with high-throughput plant phenotype potential (Pandey et al, 2017 ). Although there are many studies on nutrition monitoring using near-end hyperspectral imaging, most of them focus on quantitative monitoring and diagnosis of crop nitrogen (N), such as wheat (Mahajan et al, 2014 ; Jiang et al, 2021 ), rice (Men et al, 2021 ), maize (Furlanetto et al, 2021 ), cotton (Oliveira et al, 2020 ), rape (Liu et al, 2020a ), soybean (Chen et al, 2019 ), orange (Osco et al, 2019 , 2020a ), tea (Wang et al, 2020 ) and mango (Mahajan et al, 2021 ). At present, the quantitative monitoring research on crop K is also gradually carried out, but more studies often analyze the K together with other elements (Liu et al, 2020b ; Osco et al, 2020a , b ; Mahajan et al, 2021 ), and there are few studies only on the characteristics of single the K nutrient element.…”

Section: Introductionmentioning

confidence: 99%

“…These processes are present in all tissues and subcellular compartments of cells, which enables the precise quantification of foliar K attributes of the foliage. It has been shown that the 550–700 and 1,390–1,880-nanometer (nm) wavelengths were the best wavelengths to explain the difference in nutrient levels of N, P, and K in cotton (Oliveira et al, 2020 ; Wang et al, 2020 ). Thus, the research utilizing sensitive characteristic wavelengths or vegetation indexes to identify and estimate the K deficiency are common method in rice (Das et al, 2020 ), wheat (Hussain et al, 2017 ), and maize (Furlanetto et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Zou et al ( 2019 ) segmented broccoli seedlings from weeds and soil by extracting GLCM features and color features, and achieved higher accuracy. In the existing studies have demonstrated that the K deficiency causes discoloration of crop leaf tips and edges [such as wheat (Mahajan et al, 2014 ), rice (Sun et al, 2018 ), soybean (Ghosal et al, 2018 ), and cotton (Oliveira et al, 2020 )], then gradually spread to the center of the leaf, develop into brown spots, and finally wither and necrosis, resulting in changes in leaf color and texture (Laddi et al, 2013 ). Also, vegetation coverage and NDVI value are significantly reduced (Severtson et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

Yao

Zhang

et al. 2022

Front. Plant Sci.

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Potassium (K) is one of the most important elements influencing cotton metabolism, quality, and yield. Due to the characteristics of strong fluidity and fast redistribution of the K in plants, it leads to rapid transformation of the K lack or abundance in plant leaves; therefore, rapid and accurate estimation of potassium content in leaves (LKC, %) is a necessary prerequisite to solve the regulation of plant potassium. In this study, we concentrated on the LKC of cotton in different growth stages, an estimation model based on the combined characteristics of wavelet decomposition spectra and image was proposed, and discussed the potential of different combined features in accurate estimation of the LKC. We collected hyperspectral imaging data of 60 main-stem leaves at the budding, flowering, and boll setting stages of cotton, respectively. The original spectrum (R) is decomposed by continuous wavelet transform (CWT). The competitive adaptive reweighted sampling (CARS) and random frog (RF) algorithms combined with partial least squares regression (PLSR) model were used to determine the optimal decomposition scale and characteristic wavelengths at three growth stages. Based on the best “CWT spectra” model, the grayscale image databases were constructed, and the image features were extracted by using color moment and gray level co-occurrence matrix (GLCM). The results showed that the best decomposition scales of the three growth stages were CWT-1, 3, and 9. The best growth stage for estimating LKC in cotton was the boll setting stage, with the feature combination of “CWT-9 spectra + texture,” and its determination coefficients (R2val) and root mean squared error (RMSEval) values were 0.90 and 0.20. Compared with the single R model (R2val = 0.66, RMSEval = 0.34), the R2val increased by 0.24. Different from our hypothesis, the combined feature based on “CWT spectra + color + texture” cannot significantly improve the estimation accuracy of the model, it means that the performance of the estimation model established with more feature information is not correspondingly better. Moreover, the texture features contributed more to the improvement of model performance than color features did. These results provide a reference for rapid and non-destructive monitoring of the LKC in cotton.

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A Study on the Estimation Model of Hyperspectral Reflectivity and Leaf Nitrogen Content of Cotton Leaves

Shi

Bai

et al. 2023

IEEE Access

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Reflectance spectrometry applied to the analysis of nitrogen and potassium deficiency in cotton

Cited by 4 publications

References 0 publications

Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Gossypium hirsutum L.) under Different Nitrogen Fertilizer Application Based on Machine Learning Algorithms

Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Gossypium hirsutum L.) under Different Nitrogen Fertilizer Application Based on Machine Learning Algorithms

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

A Study on the Estimation Model of Hyperspectral Reflectivity and Leaf Nitrogen Content of Cotton Leaves

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