Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content
Keywords:
soil moisture content, swarm intelligence, characteristic wavelength selection, application, visible and near-infrared spectroscopyAbstract
Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization problems. The 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 sensor. In this study, a high-resolution spectrometer was used to obtain spectral data of different levels of soil moisture which were manually configured. Isolation Forest algorithm (iForest) was used to eliminate outliers from the data. Based 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 moisture. The 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 prediction. By 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 moisture. Integrating 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 provides the basis for the design of real-time soil moisture sensor based on VIS-NIR. Keywords: soil moisture content, swarm intelligence, characteristic wavelength selection, application, visible and near-infrared spectroscopy DOI: 10.25165/j.ijabe.20211406.6629 Citation: Zhang D X, Liu J, He X T, Yang L, Cui T, Yu T C, et al. Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content. Int J Agric & Biol Eng, 2021; 14(6): 153–161.References
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[2] Li M, Zheng L, An X, Sun H. Fast measurement and advanced sensors of soil parameters with NIR spectroscopy. Transactions of the Chinese Society for Agricultural Machinery, 2013; 44(3): 7 3-87. (in Chinese)
[3] Soriano-Disla J M, Janik L J, Viscarra Rossel R A, Macdonald L M, McLaughlin M J. The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Applied Spectroscopy Reviews, 2014; 49(2): 139-186.
[4] Stenberg B, Viscarra Rossel R A, Mouazen A M, Wetterlind J. Visible and near infrared spectroscopy in soil science. Advances in Agronomy, 2010; 107: 163-215.
[5] Zhang B. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 2016; 20(5): 1062–1090. (in Chinese)
[6] Yang L, Xu R, Lei T, Li J, Ouyang T. Design of near-infrared soil moisture measuring instrument. Transactions of the CSAE, 2015; 31(20): 1-9. (in Chinese)
[7] Peng Z, Yao Z, Wei Y, Li M, Zhen L, Liu X. Development and performance test of an in-situ soil total nitrogen-soil moisture detector based on near-infrared spectroscopy. Computers and Electronics in Agriculture, 2019; 160: 51-58.
[8] Balabin R M, Smirnov S V. Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data. Analytica Chimica Acta, 2011; 692(1): 63-72.
[9] Liu W, Bareta F, Gu X, Tong Q, Zheng L, Zhang B. Relating soil surface moisture to reflectance. Remote Sensing of Environment, 2002; 81: 238-246
[10] Yu L, Zhu Y, Hong Y, Xia T, Liu M, Zhou Y. Determination of soil moisture content by hyperspectral technology with CARS algorithm. Transactions of the CSAE, 2016; 32(22): 138-145. (in Chinese)
[11] Wu L, Wang S, He J. Study on soil moisture mechanism and establishment of model based on hyperspectral imaging technique. Spectroscopy and Spectral Analysis, 2018; 38(8): 2563-2570. (in Chinese)
[12] Bin J, Fan W, Zhou J, Li X, Liang Y. Application of intelligent optimization algorithms to wavelength selection of near-infrared spectroscopy. Spectroscopy and Spectral Analysis, 2017; 37(1): 95-102. (in Chinese)
[13] Xue L, Cai J, Li J, Liu M H. Application of particle swarm optimization (PSO) algorithm to determine dichlorvos residue on the surface of navel orange with Vis-Nir spectroscopy. Procedia Engineering, 2012; 29: 4124-4128.
[14] Cheng J H, Sun D W, Pu H B. Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle. Food Chemistry, 2016; 197: 855-863.
[15] Mavrovouniotis M, Li C H, Yang S X. A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, 2017; 33: 1-17.
[16] Lin S, Dong C, Chen M, Zhang F, Chen J. Summary of new group intelligent optimization algorithms. Computer Engineering and Applications, 2018; 54(12): 1-9. (in Chinese)
[17] Fei T L, Kai M T, Zhi-Hua Z. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 2012; 6(1): 1-39.
[18] Mouazen A M, Kuang B, De Baerdemaeker J, Ramon H. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma, 2010; 158(1): 23-31.
[19] Arora S, Singh S. Butterfly optimization algorithm: a novel approach for global optimization. Springer Berlin Heidelberg, 2019; 23(3): 715-734.
[20] Askarzadeh A. A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers and Structures, 2016; 169: 1-12.
[21] Seyedali M, Seyed M M, Andrew L. Grey wolf optimizer. Advances in Engineering Software, 2014; 69: 46-61.
[22] Seyedali M, Shahrzad S, Seyed M M, Leandro dos S.C. Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 2016; 47: 106-119.
[23] Heidari A A, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H L. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 2019; 97: 849-872.
[24] Zhao W G, Zhang Z X, Wang L Y. Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 2020; 87: 103300. doi: 10.1016/j.engappai.2019.103300.
[25] Li S M, Chen H L, Wang M J, Heidari A A, Mirjalili S. Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 2020; 111: 300-323.
[26] Seyedali M, Amir H G, Seyedeh Z M, Shahrzad S, Hossam F, Seyed M M. Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 2017; 114: 163-191.
[27] Chen T, Wang M, Huang X. Time difference of arrival passive location based on salp swarm algorithm. Journal of Electronics & Information Technology, 2018; 40(7): 1591-1597. (in Chinese)
[28] Seyedali M, Andrew L. The whale optimization algorithm. Advances in Engineering Software, 2016; 95: 51-67.
[29] Long W, Cai S, Jiao J, Tang M, Wu T. Improved whale optimization algorithm for larger scale optimization problems. Systems Engineering-Theory & Practice, 2017; 37(11): 2983-2994. (in Chinese)
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Published
2021-12-16
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Zhang, D., Liu, J., He, X., Yang, L., Cui, T., Yu, T., & Kheiry, A. N. O. (2021). Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content. International Journal of Agricultural and Biological Engineering, 14(6), 153–161. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6629
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Information Technology, Sensors and Control Systems
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