Transcriptome and hyperspectral profiling allows assessment of phosphorus nutrient status in rice under field conditions

Takehisa, Hinako; Ando, Fuminori; Takara, Yohei; Ikehata, Akifumi; Satō, Yutaka

doi:10.1111/pce.14280

Cited by 6 publications

(3 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unfortunately, to our knowledge, research on deep learning combined with hyperspectral remote sensing technology to identify rice NPK stress has not yet been reported. On the other hand, there is a limited number of studies investigating the impact of N, P, and K stress on the spectral reflectance of rice leaves [ 37 , 38 ]. The possibility of using spectral information to simultaneously evaluate the effects of N, P, and K states and their interactions in physiological responses and vegetation indices still needs to be completely investigated.…”

Section: Introductionmentioning

confidence: 99%

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Wang,

Chu,

Chen

et al. 2024

Plant Phenomics

View full text Add to dashboard Cite

Due to nutrient stress, which is an important constraint to the development of the global agricultural sector, it is now vital to timely evaluate plant health. Remote sensing technology, especially hyperspectral imaging technology, has evolved from spectral response modes to pattern recognition and vegetation monitoring. This study established a hyperspectral library of 14 NPK (nitrogen, phosphorus, potassium) nutrient stress conditions in rice. The terrestrial hyperspectral camera (SPECIM-IQ) collected 420 rice stress images and extracted as well as analyzed representative spectral reflectance curves under 14 stress modes. The canopy spectral profile characteristics, vegetation index, and principal component analysis demonstrated the differences in rice under different nutrient stresses. A transformer-based deep learning network SHCFTT (SuperPCA-HybridSN-CBAM-Feature tokenization transformer) was established for identifying nutrient stress patterns from hyperspectral images while being compared with classic support vector machines, 1D-CNN (1D-Convolutional Neural Network), and 3D-CNN. The total accuracy of the SHCFTT model under different modeling strategies and different years ranged from 93.92% to 100%, indicating the positive effect of the proposed method on improving the accuracy of identifying nutrient stress in rice.

show abstract

Section: Introductionmentioning

confidence: 99%

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Wang,

Chu,

Chen

et al. 2024

Plant Phenomics

View full text Add to dashboard Cite

show abstract

“…The GA-IPLS model has been employed to establish the correlation between cucumber leaf hyperspectral image data and leaf chlorophyll content, proposing a potassium deficiency diagnosis method based on chlorophyll distribution characteristics with an accuracy of 95% ( Shi et al., 2019 ; Shi et al., 2021 ). Reflectance spectrum and hyperspectral imaging have been used to predict the phosphorus level of rice through the establishment of a spectral curve of rice phosphorus ( Takehisa et al., 2022 ). A universal wheat single-leaf potassium monitoring model has been established by analyzing and comparing the spectral variation characteristics of wheat single-leaf slices under different potassium nutrient conditions ( Qi, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM

Xu,

Sun,

Wang

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

IntroductionAt present, machine learning and image processing technology are widely used in plant disease diagnosis. In order to address the challenges of subjectivity, cost, and timeliness associated with traditional methods of diagnosing potassium deficiency in apple tree leaves. MethodsThe study proposes a model that utilizes image processing technology and machine learning techniques to enhance the accuracy of detection during each growth period. Leaf images were collected at different growth stages and processed through denoising and segmentation. Color and shape features of the leaves were extracted and a multiple regression analysis model was used to screen for key features. Linear discriminant analysis was then employed to optimize the data and obtain the optimal shape and color feature factors of apple tree leaves during each growth period. Various machine-learning methods, including SVM, DT, and KNN, were used for the diagnosis of potassium deficiency. ResultsThe MLR-LDA-SVM model was found to be the optimal model based on comprehensive evaluation indicators. Field experiments were conducted to verify the accuracy of the diagnostic model, achieving high diagnostic accuracy during different growth periods. DiscussionThe model can accurately diagnose whether potassium deficiency exists in apple tree leaves during each growth period. This provides theoretical guidance for intelligent and precise water and fertilizer management in orchards.

show abstract

“…This feature makes it difficult to clearly determine the internal mechanism and contribution degree of climate change on the crop phenotype, which increases uncertainty in climate change impacts and projections [18]. We must consider how to use the natural environment for research [19][20][21][22][23][24], making it important to find phenotypic parameters that are readily available and more sensitive and quickly reflect complex environmental changes.…”

Section: Introductionmentioning

confidence: 99%

Response and inversion of skewness parameters to meteorological factors based on RGB model of leaf color digital image

Zhang,

Chen,

Wang

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

In the natural environment, complex and changeable meteorological factors can influence changes in the internal physiology and phenotype of crops. It is important to learn how to convert complex meteorological factor stimuli into plant perception phenotypes when analyzing the biological data obtained under the natural field condition. We restored the true gradation distribution of leaf color, which is also known as the skewed distribution of color scale, and obtained 20 multi-dimensional color gradation skewness-distribution (CGSD) parameters based on the leaf color skewness parameter system. Furthermore, we analyzed the correlation between the five corresponding meteorological factors and canopy CGSD parameters of peppers growing in a greenhouse and cabbages growing in an open air environment, built response model and inversion mode of leaf color to meteorological factors. Based on the analysis, we find a new method for correlating complex environmental problems with multi-dimensional parameters. This study provides a new idea for building a correlation model that uses leaf color as a bridge between meteorological factors and plants internal physiological state.

show abstract

Transcriptome and hyperspectral profiling allows assessment of phosphorus nutrient status in rice under field conditions

Cited by 6 publications

References 55 publications

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM

Response and inversion of skewness parameters to meteorological factors based on RGB model of leaf color digital image

Contact Info

Product

Resources

About