Multi-scale monitoring for hazard level classification of brown planthopper damage in rice using hyperspectral technique
Keywords:
brown planthopper (BPH), hazard level classification, hyperspectral technique, rice canopy, rice stem, fusion informationAbstract
The primary aim of this study was to classify the hazard level of brown Planthopper (BPH) damage in rice. Three datasets, including spectral reflectance corresponding to the sensitive wavelengths from rice canopy spectral wavelengths, rice stem spectral wavelengths, and fusion information of rice canopy and stem spectral wavelengths were used for BPH hazard level classification by using different algorithms. Datasets and algorithms were optimized by the BPH hazard level classification effects (which was evaluated by indices of accuracy, precision, recall, F1, k-value, etc.). The optimized algorithms combination was used to build hazard level classification model for spectral reflectance corresponding to the sensitive wavelength from the rice canopy spectral images. Results showed that: (1) The spectral reflectance corresponding to the sensitive wavelengths of fusion information dataset performed best in BPH hazard level classification, with the highest accuracy (99.08%), precision (99.31%), recall (98.83%), F1 (0.99), and k-value (0.99). (2) The optimum algorithms combination was Savitzky-Golay (S-G) smoothing, principal component analysis (PCA) for sensitive wavelength selection, and broad-learning system (BLS) for modeling. (3) The spectral reflectance corresponding to the sensitive wavelengths dataset of rice canopy spectral images achieved the accuracy (80.63%), precision (80.28%), recall (77.03%), F1 (0.79), and k-value (0.74) in classifying BPH hazard level by using the optimum algorithms combination. Keywords: brown planthopper (BPH), hazard level classification, hyperspectral technique, rice canopy, rice stem, fusion information DOI: 10.25165/j.ijabe.20241706.9199 Citation: Liao J, Tao W Y, Liang1 Y X, He X Y, Wang H, Zeng H Q, et al. Multi-scale monitoring for hazard level classification of brown planthopper damage in rice using hyperspectral technique. Int J Agric & Biol Eng, 2024; 17(6): 202-211 .References
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[2] Min S, Lee S W, Choi B R, Lee S H, Kwon D H. Insecticide resistance monitoring and correlation analysis to select appropriate insecticides against Nilaparvata lugens (Stl), a migratory pest in Korea. Journal of Asia-Pacific Entomology, 2014; 17(4): 711-716. DOI: 10.1016/j.aspen.2014.07.005
[3] Gomez-Chova L, Calpe J, Camps-Valls G, Martín J D, Soria E, Vila J, et al. Feature selection of hyperspectral data through local correlation and SFFS for crop classification. IEEE International Geoscience & Remote Sensing Symposium. IEEE, 2003; pp.555-557.
[4] Liang K, Ren Z Z, Song J P, Yuan R, Zhang Q. Wheat FHB resistance assessment using hyperspectral feature bandimage fusion and deep learning. Int J Agric & Biol Eng, 2024; 17(2): 240–249. DOI: 10.25165/j.ijabe.20241702.8269
[5] Carter G A, Miller R L. Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sensing of Environment, 1994; 50: 295-302.
[6] Delalieux S, Somers B, Verstraeten W W, Van Aardt J A N, Keulemans W, Coppin P. Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology. International Journal of Remote Sensing, 2009; 30(8): 1887-1912. DOI: 10.1080/01431160802541556
[7] Prasannakumar N R, Chander S, Sahoo R N, Gupta V K. Assessment of Brown Planthopper, (Nilaparvata lugens) [Stl], damage in rice using hyperspectral remote sensing. Pans Pest Articles & News Summaries, 2013; 59(3): 180-188. DOI: 10.1080/09670874.2013.808780
[8] Prasannakumar N R, Chander S, Sahoo R N. Characterization of brown planthopper damage on rice crops through hyperspectral remote sensing under field conditions. Phytoparasitica, 2014; 42(3): 387-395. DOI: 10.1007/s12600-013-0375-0
[9] Huang J R, Sun J Y, Liao H J, Liu X D. Detection of brown planthopper infestation based on SPAD and spectral data from rice under different rates of nitrogen fertilizer. Precision Agriculture, 2015; 16(2): 148-163. DOI: 10.1007/s11119-014-9367-4
[10] Tan Y, Sun J Y, Zhang B, Chen M, Liu Y, Liu X D. Sensitivity of a ratio vegetation index derived from hyperspectral remote sensing to the brown planthopper stress on rice plants. Sensors, 2019; 19(375). DOI: 10.3390/s19020375
[11] Sōgawa K. The rice brown planthopper: Feeding physiology and host plant interactions. Annual Review of Entomology, 1982; 27: 49-73. DOI: 10.1146/annurev.en.27.010182.000405
[12] Nault B A, Taylor A G, Urwiler M, Rabaey T, Hutchison W D. Neonicotinoid seed treatments for managing potato leafhopper infestations in snap bean. Crop Protection, 2004; 23(2): 147–154. DOI: 10.1016/j.cropro.2003.08.002
[13] Liu Y, Liu S, Xu J, Kong X, Xie L, Chen Y, et al. Forest pest identification based on a new dataset and convolutional neural network model with enhancement strategy. Computers and Electronics in Agriculture, 2002; 192: 1-14. DOI: 10.1016/j.compag.2021.106625
[14] Wang K, Chen K, Du H, Liu S, Xu J, Zhao J, Chen H, Liu Y. New image dataset and new negative sample judgment method for crop pest recognition based on deep learning models. Ecological informatics: an International Journal on Ecoinformatics and Computational Ecology, 2002; 69. DOI:10.1016/j.ecoinf.2022.101620
[15] Erfani S M, Rajasegarar S, Karunasekera S, Leckie C. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognition, 2016; 58(C): 121-134. Doi: 10.1016/j.patcog.2016.03.0
[16] Guo W J, Feng S, Feng Q, Li X Z, Gao X Z. Cotton leaf disease detection method based on improved SSD. Int J Agric & Biol Eng, 2024; 17(2): 211-220. DOI: 10.25165/j.ijabe.20241702.8574
[17] Chen C L P, Liu Z. Broad learning system: An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks & Learning Systems, 2018; 29(99): 10-24. DOI: 10.1109/TNNLS.2017.2716952
[18] Yasuda T, Bateni M H, Chen L, Fahrbach M, Fu G, Mirrokni V. Sequential attention for feature selection. arXiv e-prints. ICLR, Kigali Rwanda, 2023. DOI: 10.48550/arXiv.2209.14881
[19] Borzov S M, Potaturkin O I. Increasing the classification efficiency of hyperspectral images due to multi-scale spatial processing. Computer Optics, 2020; 44(6): 937-943. DOI: 10.18287/2412-6179-co-779
[20] Sun J, Yang W, Zhang M, Feng M, Ding G. Estimation of water content in corn leaves using hyperspectral data based on fractional order Savitzky-Golay derivation coupled with wavelength selection. Computers and Electronics in Agriculture, 2021; 182(1). DOI: 10.1016/j.compag.2021.105989
[21] Zimmermann B, Kohler A. Optimizing Savitzky-Golay parameters for improving spectral resolution and quantification in infrared spectroscopy. Appl Spectrosc, 2013; 67(8): 892-902. DOI: 10.1366/12-06723
[22] Wold S, Esbensen K, Geladi P. Principal component analysis, Chemometr. Intell. Lab. Syst, 1987; 2(1987): 37–52.
[23] Hervé A, Williams L J. Principal component analysis. Wiley Interdisciplinary Reviews Computational Statistics, 2010; 2(4): 433-459.
[24] Beattie J R, Esmonde-White F W L. Exploration of principal component analysis: deriving principal component analysis visually using spectra. Applied Spectroscopy, 2021; 75(4): 361-375. DOI: 10.1177/0003702820987847
[25] Rodionova O, Kucheryavskiy S, Pomerantsev A. Efficient tools for principal component analysis of complex data—A tutorial. Chemometrics and Intelligent Laboratory Systems, 2021; 213: 1-11. DOI: 10.1016/j.chemolab.2021.104304
[26] Saloni K, Deepika K, Mamta M. An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier - ScienceDirect. International Journal of Cognitive Computing in Engineering, 2021; 2: 40-46. DOI: 10.1016/j.ijcce.2021.01.001
[27] Machart P, Ralaivola L. Confusion matrix stability bounds for multiclass classification. Computer Science, 2012; 1-11. DOI:10.48550/arXiv.1202.6221
[28] Simon D, Simon D L. Analytic confusion matrix bounds for fault detection and isolation using a sum-of-squared-residuals approach. IEEE Transactions on Reliability, 2021; 59(2): 287-296. DOI: 10.1109/TR.2010.2046772
[29] Paulino J L C, Almirol L C A, Favila J M C, Aquino K A G L, Cruz A H D L, Roxas R. Multilingual sentiment analysis on short text document using semi-supervised machine learning. 5th International Conference on E-Society, E-Education and E-Technology, 2021. DOI: 10.1145/3485768.3485775
[30] Hasnain M, Pasha M F, Ghani I, Imran M, Alzahrani M Y, Budiarto R. Evaluating trust prediction and confusion matrix measures for web services ranking. IEEE Access, 2020; 8: 90847-90861. DOI: 10.1109/ACCESS.2020.2994222
[31] Lydia A, Meena K, Sekar R R, Swaminathan J N. Parkinson's disease prediction through machine learning techniques. In: Chen J I Z, Wang H, Du K L, Suma V (eds.) Machine Learning and Autonomous Systems. Smart Innovation, Systems and Technologies, 269. Springer, Singapore, 2022.
[32] Caelen O. A Bayesian interpretation of the confusion matrix. Ann. Math. Artif. Intell, 2017; 81(3-4): 429-450. DOI: 10.1007/s10472-017-9564-8
[33] Rajalakshmi R, Aravindan C. A Naive Bayes approach for URL classification with supervised feature selection and rejection framework. Computational Intelligence, 2018; 34(1): 363-396. DOI: 10.1111/coin.12158
[34] Popescu S A, Marilena J N. First-order differential equations. in: advanced mathematics for engineers and physicists. Springer, Cham, 2022.
[35] Cotter S F, Adler R, Kreutz-Delgado K. Forward sequential algorithms for best basis selection. Vision Image & Signal Processing IEE Proceedings, 1999.
[36] Ramirez S, Lizarazo I. Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images. Semantic Scholar, 2016. DOI: 10.3990/2.384
[37] Bruzzone L, Chi M, Marconcini M. A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Transactions on Geoscience & Remote Sensing, 2006; 44: 3363-3373. DOI: 10.1109/TGRS.2006.877950
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2024-12-24
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Liao, J., Tao, W., Liang, Y., He, X., Wang, H., Zeng, H., … Zang, Y. (2024). Multi-scale monitoring for hazard level classification of brown planthopper damage in rice using hyperspectral technique. International Journal of Agricultural and Biological Engineering, 17(6), 202–211. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/9199
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