Hyperspectral Estimation of Wheat Leaf Water Content Using Fractional Differentials and Successive Projection Algorithm-Back Propagation Neural Network

2019 ◽  
Vol 56 (15) ◽  
pp. 153002
Author(s):  
吾木提·艾山江 Hasan Umut ◽  
买买提·沙吾提 Sawut Mamat ◽  
马春玥 Chunyue Ma
Keyword(s):  
Neural Network ◽  
Water Content ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Leaf Water ◽  
Projection Algorithm ◽  
Leaf Water Content ◽  
Wheat Leaf ◽  
Successive Projection ◽  
Successive Projection Algorithm

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

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.

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

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.

scholarly journals Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1379 ◽  
Author(s):  
Zhuang Yang ◽  
Qu Zhou ◽  
Xiaodong Wu ◽  
Zhongyong Zhao ◽  
Chao Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Water Content ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Transformer Oil ◽  
Bpnn Model

The water content in oil is closely related to the deterioration performance of an insulation system, and accurate prediction of water content in oil is important for the stability and security level of power systems. A novel method of measuring water content in transformer oil using multi frequency ultrasonic with a back propagation neural network that was optimized by principal component analysis and genetic algorithm (PCA-GA-BPNN), is reported in this paper. 160 oil samples of different water content were investigated using the multi frequency ultrasonic detection technology. Then the multi frequency ultrasonic data were preprocessed using principal component analysis (PCA), which was implemented to obtain main principal components containing 95% of original information. After that, a genetic algorithm (GA) was incorporated to optimize the parameters for a back propagation neural network (BPNN), including the weight and threshold. Finally, the BPNN model with the optimized parameters was trained with a random 150 sets of pretreatment data, and the generalization ability of the model was tested with the remaining 10 sets. The mean squared error of the test sets was 8.65 × 10−5, with a correlation coefficient of 0.98. Results show that the developed PCA-GA-BPNN model is robust and enables accurate prediction of a water content in transformer oil using multi frequency ultrasonic technology.

scholarly journals Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Juanjuan Zhang ◽  
Wen Zhang ◽  
Shuping Xiong ◽  
Zhaoxiang Song ◽  
Wenzhong Tian ◽  
...  
Keyword(s):  
Water Content ◽  
Correlation Coefficient ◽  
Spectral Index ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Estimation Model ◽  
Full Spectrum ◽  
Calibration And Validation ◽  
Wheat Leaf ◽  
Coefficient Method

Abstract Background The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation. Methods In this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018–2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models. Results The results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750–1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R2 of 0.85 and R2 of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R2 and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307). Conclusion The two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.

scholarly journals Prediction of the Structural Yield Strength of Saline Soil in Western Jilin Province, China: A Comparison of the Back-Propagation Neural Network and Support Vector Machine Models

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1163
Author(s):  
Wei Peng ◽  
Qing Wang ◽  
Xudong Zhang ◽  
Xiaohui Sun ◽  
Yongchao Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Content ◽  
Saline Soil ◽  
Salt Content ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Soil Particles ◽  
Western Jilin ◽  
Bpnn Model

With the increase in transportation emissions, road diseases in the saline soil area of Jilin Province have become a problem that requires serious attention. In order to improve the subgrade performance, the structural yield strength (SYS) of remolded soil and its factor sensitivity are investigated in this study. Saline soils in Western Jilin are structural in the sense that the bonding strength of soil skeleton is mainly provided by the solidification bond formed by a physicochemical interaction between particles. Its SYS is influenced by its cementation type, genetic characteristics, original rock structure, and environment. Because of the high clay content in Zhenlai saline soil, the specific surface area of soil particles is large, and the surface adsorption capacity of soil particles is strong. In addition, the main cation is Na+. The cementation strength of bound water film between soil particles is thus easily affected by water content and salt content, and compaction is also an important factor affecting the strength of soil. Therefore, in this study, the back-propagation neural network (BPNN) model and a support vector machine (SVM) are used to explore the relationship of saline soil’s SYS with its compactness, water content, and salt content. In total, 120 data points collected by a high-pressure consolidation experiment are applied to building BPNN and SVM model. For eliminate redundant features, Pearson correlation coefficient (rPCC) is used as an evaluation standard of feature selection. The K-fold cross-validation method was used to avoid over fitting. To compare the performance of the BPNN and SVM models, three statistical parameters were used: the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage deviation (MAPD). The result shows that the average values of R2, RMSE, and MAPD of the BPNN model are superior to the values of the SVM. We conclude that the BPNN model is slightly better than the SVM for predicting the SYS of saline soil. Thus, the BPNN model is used to analyze the factor sensitivity of SYS. The results indicate that the influence degrees of the three parameters are as follows: water content > compactness > salt content. This study can provide a basis for estimating the structural yield pressure of soil from its basic properties, and can provide a new way to obtain parameters for geotechnical engineering, ensuring safety while maintaining symmetry in engineering costs.

Research on wheat leaf water content based on machine vision

2018 ◽  
Vol 22 (S4) ◽  
pp. 9199-9208 ◽  
Author(s):  
Xiao-Ling Ding ◽  
Li-Xin Zhao ◽  
Tian-Tian Zhou ◽  
Yi-Bin Li ◽  
Xi-Mei Huang ◽  
...  
Keyword(s):  
Machine Vision ◽  
Water Content ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Wheat Leaf

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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