Intelligent Identification and Features Attribution of Saline–Alkali-Tolerant Rice Varieties Based on Raman Spectroscopy

Ma, Bo; Liu, Chuanzeng; Hu, Jifang; Liu, Kai; Zhao, Fuyang; Wang, Junqiang; Zhao, Xiaonan; Guo, Zhenhua; Song, Lijuan; Lai, Yongcai; Tan, Kefei

doi:10.3390/plants11091210

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…where F JKR is the JKR normal contact force, N; F t c , F n d , and F t d are the tangential contact force, normal damping force, and tangential damping force, N; δ n is the normal overlap of particles, m; δ t is the tangential overlap of particles, m; α is the contact radius of particles, m; γ is the surface energy, N/m; v n and v t are the normal and tangential components of the relative velocity of particles, m/s; E * is the equivalent elastic modulus, Pa; and R * is the equivalent contact radius, m. The equivalent elastic modulus, E * , and the equivalent contact radius, R * , are defined as shown in Equations ( 6) and (7).…”

Section: Simulation Contact Modelmentioning

confidence: 99%

“…Its properties are profoundly influenced by factors such as water and organic matter content, porosity, and texture. A comprehensive review of the literature underscores the abundance of research dedicated to the physicochemical properties of soda saline-alkali soils [7][8][9][10][11][12], yet investigations into their physical and mechanical properties are notably scarce. Given the unique physicochemical attributes of soda saline-alkali soil, distinct from those of typical soil, further exploration into how these properties influence physical and mechanical characteristics is imperative.…”

Section: Introductionmentioning

confidence: 99%

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Calibration of Model Parameters for Soda Saline Soil-Subsoiling Component Interaction Based on DEM

Liu,

Wang,

Feng

et al. 2023

Applied Sciences

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To apply the discrete element method (DEM) to simulate the interaction process between soda saline–alkali soil and subsoiling component in Northeast China, establishing the soda saline–alkali soil particle model and selecting more accurate simulation parameters are important. In this paper, we studied the soda saline–alkali soil of the Songnen Plain in China. First, we studied the geometric shape of soda saline–alkali soil particles and proposed a modeling method for single soil particles based on the multisphere combination method. Considering the cohesion of soda saline–alkali soil particles, the Hertz–Mindlin with JKR (JKR) model was used as the contact model between soil particles. Then, the calibration method was used to obtain simulation parameters of soils that are difficult to obtain experimentally. We conducted soil angle of repose (AoR) tests, the Plackett–Burman (PB) tests, and steepest ascent (SA) tests in turn to perform a sensitivity analysis for microscopic contact parameters and select the parameters that have a significant effect on the response value (static AoR), i.e., soil surface energy, soil–soil static friction coefficient, and soil–soil rolling friction coefficient. Then, the optimal combination of simulation parameters was obtained via the Box–Behnken (BB) tests, using ANOVA to optimize the multiple regression equation. Finally, the optimal parameter combination was verified by the AoR test and the direct shear (DS) test. The results showed that the parameters had good adaptability for the AoR test. However, the simulation results of the DS test were significantly different from the experimental values. Therefore, the contact model needs to be further modified by adding Bonding bonds between soil particles based on the JKR model and further correcting for Rayleigh time step, shear modulus, and surface energy. By comparing the simulation and the experimental results, it was found that the simulation results obtained from both the DS test and AoR test had relatively small errors relative to physical tests, the two trends are the same, and the values are similar. This verified the feasibility and effectiveness of the soda saline–alkali soil particle modeling method and parameter selection proposed in this paper.

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Section: Simulation Contact Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibration of Model Parameters for Soda Saline Soil-Subsoiling Component Interaction Based on DEM

Liu,

Wang,

Feng

et al. 2023

Applied Sciences

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“…In this experiment, wave crests were extracted from Raman spectral data of samples at GD stage by signal.filtfilt-difference method. In combination with the Raman spectral correlation analysis of rice, seven wave crests were extracted, and the attributed substances were amylum and sugar [48,56], with amylum being the aggregation of sugar molecules [57,58]. The spectral characteristic frequency of the same species with different attributes had different offsets, i.e., the spectral crest image had different offsets [59].…”

Section: Discussion Of the Description Resultsmentioning

confidence: 99%

Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets

Zhao

Zhang

Yang

et al. 2022

Plants

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Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.

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“…Rice ( Oryza sativa L.) is a major cereal crop that feeds more than half the global population. Irrigated cultivation of rice is an effective way to manage saline-alkali conditions (Ma et al 2022 ; Zhu et al 2022 ) that has produced considerable economic benefits (Zhu et al 2022 ). Exploring the molecular mechanisms underpinning saline-alkaline stress tolerance in rice is prominent for the future development of rice varieties with enhanced saline-alkaline tolerance.…”

Section: Introductionmentioning

confidence: 99%

A Mitochondrial Localized Chaperone Regulator OsBAG6 Functions in Saline-Alkaline Stress Tolerance in Rice

Wang,

Ao,

et al. 2024

Rice

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B-cell lymphoma 2 (Bcl-2)-associated athanogene (BAG) family genes play prominent roles in regulating plant growth, development, and stress response. Although the molecular mechanism underlying BAG’s response to abiotic stress has been studied in Arabidopsis, the function of OsBAG underlying saline-alkaline stress tolerance in rice remains unclear. In this study, OsBAG6, a chaperone regulator localized to mitochondria, was identified as a novel negative regulator of saline-alkaline stress tolerance in rice. The expression level of OsBAG6 was induced by high concentration of salt, high pH, heat and abscisic acid treatments. Overexpression of OsBAG6 in rice resulted in significantly reduced plant heights, grain size, grain weight, as well as higher sensitivity to saline-alkaline stress. By contrast, the osbag6 loss-of-function mutants exhibited decreased sensitivity to saline-alkaline stress. The transcriptomic analysis uncovered differentially expressed genes related to the function of “response to oxidative stress”, “defense response”, and “secondary metabolite biosynthetic process” in the shoots and roots of OsBAG6-overexpressing transgenic lines. Furthermore, cytoplasmic levels of Ca2+ increase rapidly in plants exposed to saline-alkaline stress. OsBAG6 bound to calcium sensor OsCaM1-1 under normal conditions, which was identified by comparative interactomics, but not in the presence of elevated Ca2+. Released OsCaM1-1 saturated with Ca2+ is then able to regulate downstream stress-responsive genes as part of the response to saline-alkaline stress. OsBAG6 also interacted with energy biosynthesis and metabolic pathway proteins that are involved in plant growth and saline-alkaline stress response mechanisms. This study reveals a novel function for mitochondrial localized OsBAG6 proteins in the saline-alkaline stress response alongside OsCaM1-1.

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Intelligent Identification and Features Attribution of Saline–Alkali-Tolerant Rice Varieties Based on Raman Spectroscopy

Cited by 4 publications

References 44 publications

Calibration of Model Parameters for Soda Saline Soil-Subsoiling Component Interaction Based on DEM

Calibration of Model Parameters for Soda Saline Soil-Subsoiling Component Interaction Based on DEM

Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets

A Mitochondrial Localized Chaperone Regulator OsBAG6 Functions in Saline-Alkaline Stress Tolerance in Rice

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