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Harnessing hyperspectral imaging and machine learning techniques for accurate discrimination of peanut plants and weeds

Harnessing hyperspectral imaging and machine learning techniques for accurate discrimination of peanut plants and weeds
  • Thangella, P. A., Pasumarti, S. N., Pullakhandam, R., Geereddy, B. R. & Daggu, M. R. Differential expression of leaf proteins in four cultivars of peanut (Arachis hypogaea L.) under water stress. 3 Biotech8(1), 21 (2018).

    Google Scholar 

  • Mingrou, L., Guo, S., Ho, C. T. & Bai, N. Review on chemical compositions and biological activities of peanut (Arachis hypogeae L.). J. food biochem. 46(7), e14119 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Toomer, O. T. A comprehensive review of the value-added uses of peanut (Arachis hypogaea) skins and by-products. Crit. Rev. Food Sci. Nutr. 60, 341–350 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Rajarathinam, P., Palanisamy, G., Narayana, P. R. & Alagirisamy, M. M. Marker assisted backcross to introgress late leaf spot and rust resistance in groundnut (Arachis hypogaea L.). Mol. Biol. Rep. 50, 2411–2419 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • FAOSTAT. Production quantities of Groundnuts, excluding shelled. Food and Agricultural Organization of the United Nations, Statistics Division. https://www.fao.org/faostat/en/#data/QCL/visualize, Retrieved December. 15 2024 (2024).

  • Mehriya, M. & Choudhary, R. Effect of combined application of herbicides on the growth, yield attributes, yield and profitability of kharif groundnut [Arachis hypogaea (L.)]. Intern. J. Environ. Clim. Ch. 13, 2413–2424 (2023).

    Article 

    Google Scholar 

  • Ravi, S. et al. Quantitative variables analysis, growth and yield of groundnut (Arachis hypogaea L.) under different weed management practices. Legume Res. 47, 1396–1403 (2024).

    Google Scholar 

  • Prasad, T., Narasimha, N., Dwarakanath, N. & Krishnamurthy, K. Efficacy of oxyfluorfen for weed control in irrigated groundnut. Agric. Rev. 32, 155–171 (1987).

    CAS 

    Google Scholar 

  • Pervaiz, R. Herbicide strategies for weed control in rice cultivation: Current practices and future directions. Haya Saudi J. Life Sci 9(114), 129 (2024).

    Google Scholar 

  • Ojelade, O. B. et al. Intra-row spacing and weed control influence growth and yield of groundnut (Arachis hypogea L.). Adv. Agric. Sci. 6, 1–11 (2018).

    Google Scholar 

  • Abbas, T., Zahir, Z. A., Naveed, M. & Kremer, R. J. Limitations of existing weed control practices necessitate development of alternative techniques based on biological approaches. Adv. Agron. 147, 239–280 (2018).

    Article 

    Google Scholar 

  • Padhiary, M., Saha, D., Kumar, R., Sethi, L. N. & Kumar, A. Enhancing precision agriculture: A comprehensive review of machine learning and ai vision applications in all-terrain vehicle for farm automation. Smart. Agric. Technol. 8, 100483 (2024).

    Article 

    Google Scholar 

  • Pérez-Ortiz, M. et al. Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery. Expert. Syst. Appl 47, 85–94. (2016).

    Article 

    Google Scholar 

  • Oerke, E. C., Gerhards, R., Menz, G., Sikora, R .A. Precision Crop Protection – the Challenge and Use of Heterogeneity (2010).

  • Shaner, D. L. & Beckie, H. J. The future for weed control and technology. Pest. Manag. Sci. 70, 1329–1339. (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Slaughter, D. C., Giles, D. K. & Downey, D. Autonomous robotic weed control systems: A review. Comput. Electron. Agric. 61, 63–78. (2008).

    Article 

    Google Scholar 

  • Khonina, S. N., Kazanskiy, N. L., Oseledets, I. V., Nikonorov, A. V. & Butt, M. A. Synergy between artificial intelligence and hyperspectral imagining—a review. Technol 12, 163 (2024).

    Google Scholar 

  • Gowen, A. A., O’Donnell, C. P., Cullen, P. J., Downey, G. & Frias, J. M. Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trend. Food Sci. Technol. 18, 590–598. (2007).

    Article 
    CAS 

    Google Scholar 

  • Mahlein, A. K. et al. Development of spectral indices for detecting and identifying plant diseases. Remot. Sens. Environ. 128, 21–30. (2013).

    Article 
    ADS 

    Google Scholar 

  • Wang, B. et al. The applications of hyperspectral imaging technology for agricultural products quality analysis: A review. Food Rev. Intl. 39, 1043–1062 (2023).

    Article 

    Google Scholar 

  • Ram, B. G., Oduor, P., Igathinathane, C., Howatt, K. & Sun, X. A systematic review of hyperspectral imaging in precision agriculture: analysis of its current state and future prospects. Comput. Electron. Agric. 222, 109037 (2024).

    Article 

    Google Scholar 

  • Lim, H.-H., Cheon, E. & Lee, S.-R. Machine learning and hyperspectral imaging to predict soil water content: Methodology and field validation. Earth Sci. Inf. 18, 109 (2025).

    Article 
    ADS 

    Google Scholar 

  • Peng, Y. et al. Estimation of soil nutrient content using hyperspectral data. Agriculture 11, 1129 (2021).

    Article 
    CAS 

    Google Scholar 

  • Amanor, I. N., Ricardo, O. A. & Noguchi, N. Assessment of remote sensing in measuring soil parameters for precision tillage. J. Terrramech. 113, 100973 (2024).

    Article 

    Google Scholar 

  • Antony, M. M., Sandeep, C. S., Bijeesh, M. & Matham, M. V., In Photonic Technologies in Plant and Agricultural Science. 108–114 (SPIE).

  • Huang, Y., Lee, M. A., Thomson, S. J. & Reddy, K. N. Ground-based hyperspectral remote sensing for weed management in crop production. Intern. J. Agric. Biol. Eng. 9, 98–109 (2016).

    CAS 

    Google Scholar 

  • Yu, F. et al. Research on precise fertilization method of rice tillering stage based on UAV hyperspectral remote sensing prescription map. Agronomy 12, 2893 (2022).

    Article 
    CAS 

    Google Scholar 

  • Cao, J. et al. Identifying mangrove species using field close-range snapshot hyperspectral imaging and machine-learning techniques. Remot. Sens. 10, 2047 (2018).

    Article 
    ADS 

    Google Scholar 

  • İrik, H. A., Ropelewska, E. & Çetin, N. Using spectral vegetation indices and machine learning models for predicting the yield of sugar beet (Beta vulgaris L.) under different irrigation treatments. Comput. Electron. Agric. 221, 109019 (2024).

    Article 

    Google Scholar 

  • Hu, Y. et al. Nondestructive classification of maize moldy seeds by hyperspectral imaging and optimal machine learning algorithms. Sens 22, 6064 (2022).

    Article 
    ADS 

    Google Scholar 

  • Jordan, M. & Mitchell, T. M. Machine learning: Trends, perspectives, and prospects. Sci. (New York, N.Y.) 349(255), 260. (2015).

    Article 
    MathSciNet 
    CAS 

    Google Scholar 

  • Morales, E. F. & Escalante, H. J. in Biosignal processing and classification using computational learning and intelligence. Elsevier 111, 129 (2022).

    Google Scholar 

  • Miao, Y., Mulla, D. J., & Huang, Y. In Remote Sensing Handbook, Volume III 229–254 (CRC Press, 2024).

  • Khan, A., Vibhute, A. D., Mali, S. & Patil, C. H. A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Eco Inform. 69, 101678. (2022).

    Article 

    Google Scholar 

  • Ang, K.L.-M. & Seng, J. K. P. Big data and machine learning with hyperspectral information in agriculture. IEEE Access 9, 36699–36718 (2021).

    Article 

    Google Scholar 

  • Su, W.-H. Advanced machine learning in point spectroscopy, RGB-and hyperspectral-imaging for automatic discriminations of crops and weeds: A review. Smart Citie. 3, 767–792 (2020).

    Article 

    Google Scholar 

  • Li, Y. et al. Identification of weeds based on hyperspectral imaging and machine learning. Front. Plant. Sci. 11, 611622 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Qiao, X. et al. A method of invasive alien plant identification based on hyperspectral images. Agronomy 12, 2825 (2022).

    Article 

    Google Scholar 

  • Huang, Y. et al. Hyperspectral imaging for identification of an invasive plant mikania micrantha kunth. Front. Plant. Sci. 12, 626516. (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ma, J., Pang, L., Yan, L. & Xiao, J. Detection of black spot of rose based on hyperspectral imaging and convolutional neural network. AgriEngineering 2, 556–567 (2020).

    Article 

    Google Scholar 

  • Cozzolino, D., Williams, P. & Hoffman, L. An overview of pre-processing methods available for hyperspectral imaging applications. Microchem J 193, 109129 (2023).

    Article 
    CAS 

    Google Scholar 

  • Moradifar, M. & Shahbahrami, A. In International Conference on Machine Vision and Image Processing (MVIP). 1–6 (IEEE) (2020).

  • Cheng, C., Wei, Y., Sun, X. & Zhou, Y. Estimation of chlorophyll-a concentration in turbid lake using spectral smoothing and derivative analysis. Int. J. Environ. Res. Pub. Health 10, 2979–2994 (2013).

    Article 
    CAS 

    Google Scholar 

  • Ishibashi, K., Iwasaki, T., Otomasa, S. & Yada, K. Model selection for financial statement analysis: Variable selection with data mining technique. Proc. Comput. Sci. 96, 1681–1690 (2016).

    Article 

    Google Scholar 

  • Badreldin, N., Cheng, X. & Youssef, A. An overview of software sensor applications in biosystem monitoring and control. Sensors 24, 6738 (2024).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Shi, S. et al. Land cover classification with multispectral LiDAR based on multi-scale spatial and spectral feature selection. Remote. Sens. 13, 4118 (2021).

    Article 
    ADS 

    Google Scholar 

  • Ge, R. et al. McTwo: A two-step feature selection algorithm based on maximal information coefficient. BMC Bioinform. 17, 142 (2016).

    Article 

    Google Scholar 

  • Tharwat, A., Gaber, T., Ibrahim, A. & Hassanien, A. E. Linear discriminant analysis: A detailed tutorial. AI Commun. 30, 169–190 (2017).

    Article 
    MathSciNet 

    Google Scholar 

  • Zhang, X. et al. A novel method on recognizing drum load of elastic tooth drum pepper harvester based on CEEMDAN-KPCA-SVM. Agriculture 14, 1114 (2024).

    Article 
    CAS 

    Google Scholar 

  • Vasantha, A. D., Paul, P. P. & Usha, M. In 6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). 139–149 (IEEE, 2022).

  • Sathya, P. & Gnanasekaran, P. Ensemble feature selection framework for paddy yield prediction in cauvery basin using machine learning classifiers. Cogent. Eng. 10, 2250061 (2023).

    Article 

    Google Scholar 

  • Zualkernan, I., Abuhani, D. A., Hussain, M. H., Khan, J. & ElMohandes, M. Machine learning for precision agriculture using imagery from unmanned aerial vehicles (uavs): A survey. Drones 7, 382 (2023).

    Article 

    Google Scholar 

  • Shorewala, S., Ashfaque, A., Sidharth, R. & Verma, U. Weed density and distribution estimation for precision agriculture using semi-supervised learning. IEEE access 9, 27971–27986 (2021).

    Article 

    Google Scholar 

  • Zhang, H. et al. Weed detection in peanut fields based on machine vision. Agriculture 12, 1541 (2022).

    Article 

    Google Scholar 

  • Zolotukhina, A. et al Evaluation of Leaf Chlorophyll Content from Spectral Scanning Hyperspectral Data: Multi-Crop Study. SSRN 4570870.

  • Nevalainen, O. et al. Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remot. Sens. 9, 185 (2017).

    Article 
    ADS 

    Google Scholar 

  • Su, J. et al. Spectral analysis and mapping of blackgrass weed by leveraging machine learning and UAV multispectral imagery. Comput. Electron. Agric. 192, 106621 (2022).

    Article 

    Google Scholar 

  • Rogers, M., Blanc-Talon, J., Urschler, M. & Delmas, P. Wavelength and texture feature selection for hyperspectral imaging: A systematic literature review. J. Food Meas. Charact. 17, 6039–6064 (2023).

    Article 

    Google Scholar 

  • Che’YaDunwoody, N. N. E. & Gupta, M. Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery. Agronomy 11, 1435 (2021).

    Article 

    Google Scholar 

  • Panneton, B., Guillaume, S., Roger, J.-M. & Samson, G. Improved discrimination between monocotyledonous and dicotyledonous plants for weed control based on the blue-green region of ultraviolet-induced fluorescence spectra. Appl. Spectrosc. 64, 30–36 (2010).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Sun, J. et al. Estimation of water content in corn leaves using hyperspectral data based on fractional order savitzky-golay derivation coupled with wavelength selection. Comput. Electron. Agric. 182, 105989 (2021).

    Article 

    Google Scholar 

  • Ahmed, M. R. et al. Multiclass classification on soybean and weed species using a customized greenhouse robotic and hyperspectral combination system. J. ASABE 65, 1071–1080 (2022).

    Article 
    CAS 

    Google Scholar 

  • Haghbin, N., Bakhshipour, A., Zareiforoush, H. & Mousanejad, S. Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics. Plant. Method. 19, 53 (2023).

    Article 
    CAS 

    Google Scholar 

  • Khomeirani, H. H., Rahimi-Ajdadi, F. & Mollazade, K. Identifying key wavelengths for distinguishing between two narrow-leaved weeds and rice seedlings using hyperspectral imaging. Measurement 118336 (2025).

  • Zhang, Y. et al. Automated spectral feature extraction from hyperspectral images to differentiate weedy rice and barnyard grass from a rice crop. Comput. Electron. Agric. 159, 42–49 (2019).

    Article 

    Google Scholar 

  • Ram, B. G. et al. Palmer amaranth identification using hyperspectral imaging and machine learning technologies in soybean field. Comput. Electron. Agric. 215, 108444 (2023).

    Article 

    Google Scholar 

  • Xu, L., Yan, P. & Chang, T. In 9th international conference on pattern recognition. 706,707,708–706,707,708 (IEEE Computer Society).

  • Park, J.-J. et al. Non-destructive quantification of sea lettuce in laver using hyperspectral imaging with hybrid spectral feature selection techniques. Food Biosci. 66, 106272 (2025).

    Article 
    CAS 

    Google Scholar 

  • Guo, Z. et al. Application of visible-near-infrared hyperspectral imaging technology coupled with wavelength selection algorithm for rapid determination of moisture content of soybean seeds. J. Food Compos. Anal. 116, 105048 (2023).

    Article 
    CAS 

    Google Scholar 

  • Chen, Z. et al. A leaf chlorophyll content estimation method for populus deltoides (populus deltoides marshall) using ensembled feature selection framework and unmanned aerial vehicle hyperspectral data. Forests 15, 1971 (2024).

    Article 

    Google Scholar 

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