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Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content

International Journal of Agricultural and Biological Engineering 2021年第6期14卷 153-161页

作者： Dongxing Zhang Jiang Liu Li Yang Tao Cui Xiantao He Tiancheng Yu Abdalla N.O.Kheiry College of Engineering China Agricultural UniversityBeijing 100083China The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China Beijing 100083China Department of Agricultural Engineering College of Agricultural StudiesSudan University of Science and TechnologyKhartoum 999129Sudan

Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization *** application of the swarm intelligence algorithms to visible and near-infrared(VIS-NIR)spectral analysis of soil moisture can contribute to the optimization of the soil moisture prediction model and the development of the real-time soil moisture *** this study,a high-resolution spectrometer was used to obtain spectral data of different levels of soil moisture which were manually *** Forest algorithm(iForest)was used to eliminate outliers from the *** on the root mean square error of prediction RMSEP of Back Propagation Neural Network(BPNN)model results,a series of new swarm intelligence algorithms,including Manta Ray Foraging Optimization(MRFO),Slime Mould Algorithm(SMA),etc.,were used to select the characteristic wavelengths of soil *** analysis results showed that MRFO owned the best performance if only from the predictive capability perspective and SMA had a better performance when considering the proportion of the selecting wavelengths and the results of the model *** comparing and analyzing the modeling results of traditional intelligence algorithms Genetic Algorithm(GA)and Particle Swarm Optimization(PSO),it was found that the new swarm intelligence had a better performance in selecting the characteristic wavelengths of soil *** the results of all intelligence algorithms used,soil moisture sensitive wavelengths were selected as 490 nm,513 nm,543 nm,900 nm and 926 nm,which provide the basis for the design of real-time soil moisture sensor based on VIS-NIR.

关键词： soil moisture content swarm intelligence characteristic wavelength selection application visible and near-infrared spectroscopy

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Comparison of algorithms for monitoring wheat powdery mildew using multi-angular remote sensing data

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The Crop Journal 2022年第5期10卷 1312-1322页

作者： Li Song Luyuan Wang Zheqing Yang Li He Ziheng Feng Jianzhao Duan Wei Feng Tiancai Guo State Key Laboratory of Wheat and Maize Crop Science Agronomy CollegeHenan Agriculture UniversityZhengzhou 450046HenanChina Information and Management Science College Henan Agricultural UniversityZhengzhou 450046HenanChina CIMMYT-China Wheat and Maize Joint Research Center State Key Laboratory of Wheat and Maize Crop ScienceHenan Agricultural UniversityZhengzhou 450046HenanChina

Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith *** accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.

关键词： characteristic wavelength selection Estimation model Machine learning Multi-angular remote sensing Wheat powdery mildew

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Rapid Determination of Hemicellulose Content in Corn Stalks by Near-infrared Spectroscopy Based on Dung Beetle Optimizer

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Agricultural Biotechnology 2024年第5期13卷 83-85,92页

作者： Baihong TONG Jinming LIU Jianfei SHI College of Information and Electrical Engineering Heilongjiang Bayi Agricultural UniversityDaqing 163000China

Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new *** is an important component in corn stalks,and it is very important to determine its content in corn *** this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was *** order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of *** modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms.

关键词： Hemicellulose Near-infrared spectrum characteristic wavelength selection Intelligent optimization algorithm Dung beetle algorithm

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