Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters

Water content in plants is one of the most common biochemical parameters limiting efficiency of photosynthesis and crop productivity. Therefore, it has very important meaning to predict the water content rapidly and nondestructively. The objective of this study was to investigate the feasibility of detecting the water content in the leaf using the diffuse reflectance spectra limited in the VIS/NIR region (400–1100 nm), which could be used to determine other biochemical parameters such as chlorophyll and nitrogen content. The experiment with leaves in different water stress was conducted. The statistical test result indicated that the determination of water content in leaf could be successfully performed by VIS/NIR spectroscopy combined with chemometrics method. The performances of different pretreatment methods were compared. The model with best performance was obtained from the first derivative spectra. In order to make the calibration model more parsimonious and stable, a hybrid wavelength selection method was proposed to extract the efficient feature wavelength. Under the optimal condition, an RMSEP of 0.73% with 25 variables was obtained for water content prediction using extern validation. The conclusions presented could lead to the development of portable instrument for synchronous detecting water content and other biochemical parameters rapidly and nondestructively.

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Evaluation of Resistance of Winter Wheat to Fusarium acuminatum by Inoculation of Seedling Roots with Single, Germinated Macroconidia

Plant Disease ◽

10.1094/pdis.1998.82.3.300 ◽

1998 ◽

Vol 82 (3) ◽

pp. 300-302 ◽

Cited By ~ 12

Author(s):

M. Mergoum ◽

J. P. Hill ◽

J. S. Quick

Keyword(s):

Water Stress ◽

Winter Wheat ◽

Water Content ◽

Root Rot ◽

Leaf Water ◽

Leaf Water Content ◽

Ion Leakage ◽

Wheat Cultivars ◽

Fusarium Acuminatum ◽

Seedling Roots

Fusarium acuminatum is one of the causal agents of dryland root rot of winter wheat in Colorado. The effect of F. acuminatum seedling root infection, recorded at heading, on winter wheat cultivars Sandy and CO84 was investigated in the greenhouse. Winter wheat seeds were surface disinfested, germinated, and vernalized. Vernalized seedling roots were inoculated by placing a single, germinated macroconidium of F. acuminatum on the largest root. Inoculated and non-inoculated vernalized seedlings were transplanted to pots and half the plants subjected to water stress. Inoculated plants had significantly lower survival rates and, at maturity, lower relative leaf water content, fewer tillers, shorter plant height, and higher cell ion leakage than non-inoculated plants. Wheat cultivars differed significantly for most traits studied. CO84 was susceptible whereas Sandy was more tolerant of the pathogen, particularly under water stress conditions. These results suggest that relative leaf water content, cell ion leakage, and to some extent seedling survival may be useful attributes for evaluation of resistance to the root rot pathogen.

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Comparison of the Prediction of Winter Wheat Leaf Water Content by Using New Hyperspectral Index and Machine Learning Models

10.21203/rs.3.rs-96551/v1 ◽

2020 ◽

Author(s):

Juanjuan Zhang ◽

Wen Zhang ◽

Shuping Xiong ◽

Zhaoxiang Song ◽

Wenzhong Tian ◽

...

Keyword(s):

Winter Wheat ◽

Water Content ◽

Spectral Index ◽

Spectral Reflectance ◽

Leaf Water ◽

Leaf Water Content ◽

Full Band ◽

Wheat Leaf ◽

Canopy Spectral Reflectance ◽

Better Than

Abstract In this study, hyperspectral technology was used to establish the winter wheat leaf water content inversion model to provide technical reference for winter wheat precision irrigation. In a field experiment, seven different wheat varieties for different irrigation times were treated during two consecutive years. The data onto canopy spectral reflectance and leaf water content (LWC) of winter wheat were collected. Five different modeling methods, Spectral index, partial least squares (PLSR), random forest (RF), extreme random tree (ERT) and k-nearest neighbor (KNN) were used to construct LWC estimation models. The results showed that the canopy spectral reflectance was directly proportional to the irrigation times, especially in the near infrared band. As for LWC, the prediction effect of the newly differential spectral index DVI (R1185, R1308) is better than the existing spectral index, and R2 are 0.78. Because of the large amount of hyperspectral data. The correlation coefficient method (CA) and loading weight (x-Lw) are used to select the water characteristic bands from the full band. The results show that the accuracy of the model based on the characteristic band is not significantly lower than that of the full band. Among these models, the ERT- x-Lw model performs best (R2 and RMSE of 0.88 and 1.81; 0.84 and 1.62 for calibration and validation, respectively). In addition, the accuracy of LWC estimation model constructed by ERT-x-Lw was better than that of DVI (R1185, R1307). The results provide technical reference and basis for crop water monitoring and diagnosis under similar production conditions.

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Determination of Leaf Water Content with a Portable NIRS System Based on Deep Learning and Information Fusion Analysis

Transactions of the ASABE ◽

10.13031/trans.13989 ◽

2021 ◽

Vol 64 (1) ◽

pp. 127-135

Author(s):

Lei Zhou ◽

Chu Zhang ◽

Mohamed Farag Taha ◽

Zhengjun Qiu ◽

Yong He

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Water Content ◽

Decision Fusion ◽

Machine Intelligence ◽

Partial Least Square ◽

Leaf Water ◽

Leaf Water Content

HighlightsA portable NIRS system with local computing hardware was developed for leaf water content determination.The proposed convolutional neural network for regression showed a satisfactory performance.Decision fusion of multiple regression models achieved a higher precision than single models.All of the devices and machine intelligence algorithms were integrated into the system.Software was developed for system control and user interface.Abstract. Spectroscopy has been widely used as a valid non-destructive technique for the determination of crop physiological parameters. In this study, a portable near-infrared spectroscopy (NIRS) system was developed for rapid measurement of rape (Brassica campestris) leaf water content. An integrated spectrometer (900 to 1700 nm) was used to collect the spectra. A Wi-Fi module was adopted for driving the spectrometer and realizing data communication. The NVIDIA Jetson Nano developer kit was employed to handle the received spectra and perform computing tasks. Three embedded spectral analysis models, including support vector regression (SVR), partial least square regression (PLSR), and deep convolutional neural network for regression (CNN-R), and decision fusions of these methods were built and compared. The results demonstrated that the separate models produced satisfactory predictions. The proposed system achieved the highest precision based on the fusion of PLSR and CNN-R. The hardware devices and analytical algorithms were all integrated into the proposed portable system, and the tested samples were collected from an actual field environment, which shows great potential of the system for outdoor applications. Keywords: Decision fusion, Deep learning, Leaf water content, Local computing, Portable NIRS system.

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