Controlling weed infestation is pivotal to achieving the maximum yield in paddy fields. At a time of exponential human population growth and depleting arable land mass, finding the solution to this problem is crucial. For a long time, herbicides have been the most favoured approach for weed control due to their efficacy and ease of application. However, adverse effects on the environment due to the excessive use of herbicides have prompted more cautious and effective herbicide usage. Many weed species tend to dominate the field, and the weed thrived in patches, rendering conventional broad herbicide spraying futile. Site-specific weed management (SSWM) consists of two strategies: weed mapping and selective herbicide application. Since its introduction into the agriculture sector, unmanned aerial vehicles (UAV) have become the platform of choice for carrying both the remote sensing system for weed mapping and the selective application of herbicide. Red-Green-Blue (RGB), multispectral and hyperspectral sensors on UAVs enable highly accurate weed mapping. In Malaysia, adopting this technology is highly possible, given the nature of government-administrated rice cultivation. This review provides insight into the weed management practice using remote sensing techniques on UAV platforms with potential applications in Malaysia 's paddy field. It also discusses the recent works on weed mapping with imaging remote sensing on a UAV platform.

• Abidin, I. S. Z., Haseeb, M., Islam, R., & Chiat, L. W. (2022). Role of technology adoption, labor force and capital formation on the rice production in Malaysia. AgBioForum, 24(1), 41–49.

• Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, 9(11), Article 1110. https://doi.org/10.3390/rs9111110

• Alam, M. K., Bell, R. W., Hasanuzzaman, M., Salahin, N., Rashid, M. H., Akter, N., Akhter, S., Islam, M. S., Islam, S., Naznin, S., Anik, M. F. A., Mosiur Rahman Bhuyin Apu, M., Saif, H. Bin, Alam, M. J., & Khatun, M. F. (2020). Rice (Oryza sativa L.) establishment techniques and their implications for soil properties, global warming potential mitigation and crop yields. Agronomy, 10(6), Article 888. https://doi.org/10.3390/agronomy10060888

• Askari, M. S., McCarthy, T., Magee, A., & Murphy, D. J. (2019). Evaluation of grass quality under different soil management scenarios using remote sensing techniques. Remote Sensing, 11(15), Article 1835. https://doi.org/10.3390/rs11151835

• Benos, L., Tagarakis A. C., Dolias G., Berruto R., Kateris D., & Bochtis D. (2021) Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), Article 3758. https://doi.org/10.3390/s21113758

• Bullock, D., Mangeni, A., Kolkman, J. M., Nelson, R. J., & Gore, M. A. (2019). Automated weed detection in aerial imagery with context. ArXiv Preprint. https://doi.org/10.48550/arXiv.1910.00652

• Busi, R., Nguyen, N. K., Chauhan, B. S., Vidotto, F., Tabacchi, M., & Powles, S. B. (2017). Can herbicide safeners allow selective control of weedy rice infesting rice crops? Pest Management Science, 73(1), 71–77. https://doi.org/10.1002/ps.4411

• Cai, C., Yang, H., Zhang, L., & Cao, W. (2022). Potential yield of world rice under global warming based on the ARIMA-TR model. Atmosphere, 13(8), Article 1336. https://doi.org/10.3390/atmos13081336

• Casa, R., Pascucci, S., Pignatti, S., Palombo, A., Nanni, U., Harfouche, A., Laura, L., Di Rocco, M., & Fantozzi, P. (2019). UAV-based hyperspectral imaging for weed discrimination in maize. In J. V. Stafford (Ed.), Precision Agriculture 2019 (pp. 365-371). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_45

• Che’ya, N. N., Dunwoody, E., & Gupta, M. (2021). Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery. Agronomy, 11(7), Article 1435. https://doi.org/10.3390/agronomy11071435

• Chen, P., Ouyang, F., Zhang, Y., & Lan, Y. (2022). Preliminary evaluation of spraying quality of multi-unmanned aerial vehicle (UAV) close formation spraying. Agriculture, 12(8), Article 1149. https://doi.org/10.3390/agriculture12081149

• Chen, S., Lan, Y., Zhou, Z., Ouyang, F., Wang, G., Huang, X., Deng, X., & Cheng, S. (2020). Effect of droplet size parameters on droplet deposition and drift of aerial spraying by using plant protection UAV. Agronomy, 10(2), Article 195. https://doi.org/10.3390/agronomy10020195

• de Camargo, T., Schirrmann, M., Landwehr, N., Dammer, K. H., & Pflanz, M. (2021). Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops. Remote Sensing, 13(9), Article 1704. https://doi.org/10.3390/rs13091704

• de Castro, A. I., Torres-Sánchez, J., Peña, J. M., Jiménez-Brenes, F. M., Csillik, O., & López-Granados, F. (2018). An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sensing, 10(2), Article 285. https://doi.org/10.3390/rs10020285

• Deng, J., Zhong, Z., Huang, H., Lan, Y., Han, Y., & Zhang, Y. (2020). Lightweight semantic segmentation network for real-time weed mapping using unmanned aerial vehicles. Applied Sciences, 10(20), Article 7132. https://doi.org/10.3390/app10207132

• Bah, M. D., Hafiane, A., & Canals, R. (2018). Deep learning with unsupervised data labeling for weed detection in line crops in UAV images. Remote Sensing, 10(11), Article 1690. https://doi.org/10.3390/rs10111690

• Dilipkumar, M., Ahmad-Hamdani, M. S., Rahim, H., Chuah, T. S., & Burgos, N. R. (2021). Survey on weedy rice (Oryza spp.) management practice and adoption of Clearfield® rice technology in Peninsular Malaysia. Weed Science, 69(5), 558–564. https://doi.org/10.1017/wsc.2021.16

• Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87. https://doi.org/10.1145/2347736.2347755

• Eddy, P. R., Smith, A. M., Hill, B. D., Peddle, D. R., Coburn, C. A., & Blackshaw, R. E. (2014). Weed and crop discrimination using hyperspectral image data and reduced bandsets. Canadian Journal of Remote Sensing, 39(6), 481–490. https://doi.org/10.5589/m14-001

• Eide, A., Koparan, C., Zhang, Y., Ostlie, M., Howatt, K., & Sun, X. (2021). UAV-Assisted thermal infrared and multispectral imaging of weed canopies for glyphosate resistance detection. Remote Sensing, 13(22), Article 4606. https://doi.org/10.3390/rs13224606

• Esposito, M., Crimaldi, M., Cirillo, V., Sarghini, F., & Maggio, A. (2021). Drone and sensor technology for sustainable weed management: A review. Chemical and Biological Technologies in Agriculture, 8(1), 1–11. https://doi.org/10.1186/s40538-021-00217-8

• Fraccaro, P., Butt, J., Edwards, B., Freckleton, R. P., Childs, D. Z., Reusch, K., & Comont, D. (2022). A deep learning application to map weed spatial extent from unmanned aerial vehicles imagery. Remote Sensing, 14(17), Article 973. https://doi.org/10.3390/rs14174197

• Furukawa, F., Laneng, L. A., Ando, H., Yoshimura, N., Kaneko, M., & Morimoto, J. (2021). Comparison of RGB and multispectral unmanned aerial vehicle for monitoring vegetation coverage changes on a landslide area. Drones, 5(3), Article 97. https://doi.org/10.3390/drones5030097

• Gao, J., Liao, W., Nuyttens, D., Lootens, P., Vangeyte, J., Pižurica, A., He, Y., & Pieters, J. G. (2018). Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery. International Journal of Applied Earth Observation and Geoinformation, 67, 43–53. https://doi.org/10.1016/j.jag.2017.12.012

• Gašparović, M., Zrinjski, M., Barković, Đ., & Radočaj, D. (2020). An automatic method for weed mapping in oat fields based on UAV imagery. Computers and Electronics in Agriculture, 173, Article 105385. https://doi.org/10.1016/j.compag.2020.105385

• Gerhards, R., Andújar Sanchez, D., Hamouz, P., Peteinatos, G. G., Christensen, S., & Fernandez-Quintanilla, C. (2022). Advances in site-specific weed management in agriculture - A review. Weed Research, 62(2), 123–133. https://doi.org/10.1111/wre.12526

• Guo Y, Chen S, Li X, Cunha M, Jayavelu S, Cammarano D, Fu Y. (2022). Machine learning-based approaches for predicting SPAD values of maize using multi-spectral images. Remote Sensing, 14(6), Article 1337. https://doi.org/10.3390/rs14061337

• Hanif, A. S., Han, X., & Yu, S. H. (2022). Independent control spraying system for UAV-based precise variable sprayer: A review. Drones, 6(12), Article 383. https://doi.org/10.3390/drones6120383

• Hao, Z., Li, M., Yang, W., & Li, X. (in press). Evaluation of UAV spraying quality based on 1D-CNN model and wireless multi-sensors system. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2022.07.004

• Haq, M. A. (2021). CNN based automated weed detection system using UAV imagery. Computer Systems Science and Engineering, 42(2), 837–849. https://doi.org/10.32604/csse.2022.023016

• Hasan, M., Mokhtar, A. S., Mahmud, K., Berahim, Z., Rosli, A. M., Hamdan, H., Motmainna, M., & Ahmad-Hamdani, M. S. (2022). Physiological and biochemical responses of selected weed and crop species to the plant-based bioherbicide WeedLock. Scientific Reports, 12(1), Article 19602. https://doi.org/10.1038/s41598-022-24144-2

• Hasan, M., Ahmad-Hamdani, M. S., Rosli, A. M., & Hamdan, H. (2021). Bioherbicides: An eco-friendly tool for sustainable weed management. Plants, 10(6), Article 1212. https://doi.org/10.3390/plants10061212

• Hasan, M., Mokhtar, A. S., Rosli, A. M., Hamdan, H., Motmainna, M., & Ahmad-Hamdani, M. S. (2021). Weed control efficacy and crop-weed selectivity of a new bioherbicide WeedLock. Agronomy, 11(8), Article 1488. https://doi.org/10.3390/agronomy11081488

• Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., Wen, S., Zhang, H., & Zhang, Y. (2018a). Accurate weed mapping and prescription map generation based on fully convolutional networks using UAV imagery. Sensors, 18(10), Article 3299. https://doi.org/10.3390/s18103299

• Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., & Zhang, L. (2018b). A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PLoS ONE, 13(4), Article e0196302. https://doi.org/10.1371/journal.pone.0196302

• Huang, H., Lan, Y., Deng, J., Yang, A., Deng, X., Zhang, L., & Wen, S. (2018). A semantic labeling approach for accurate weed mapping of high resolution UAV imagery. Sensors, 18(7), Article 2113. https://doi.org/10.3390/s18072113

• Huang, Y., Reddy, K. N., Fletcher, R. S., & Pennington, D. (2018). UAV low-altitude remote sensing for precision weed management. Weed Technology, 32(1), 2–6. https://doi.org/10.1017/wet.2017.89

• Huang, H., Lan, Y., Yang, A., Zhang, Y., Wen, S., & Deng, J. (2020). Deep learning versus object-based image analysis (OBIA) in weed mapping of UAV imagery. International Journal of Remote Sensing, 41(9), 3446–3479. https://doi.org/10.1080/01431161.2019.1706112

• Hunt, E. R., & Daughtry, C. S. T. (2018). What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing, 39(15–16), 5345–5376. https://doi.org/10.1080/01431161.2017.1410300

• Júnior, P. C. P., Monteiro, A., Ribeiro, R. da L., Sobieranski, A. C., & von-Wangenheim, A. (2020). Comparison of classical computer vision vs. Convolutional neural networks for weed mapping in aerial images. Revista de Informatica Teorica e Aplicada, 27(4), 20–33. https://doi.org/10.22456/2175-2745.97835

• Kawamura, K., Asai, H., Yasuda, T., Soisouvanh, P., & Phongchanmixay, S. (2021). Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm. Plant Production Science, 24(2), 198–215. https://doi.org/10.1080/1343943X.2020.1829490

• Khan, S., Tufail, M., Khan, M. T., Khan, Z. A., Iqbal, J., & Alam, M. (2021). A novel semi-supervised framework for UAV based crop/weed classification. PLoS ONE, 16(5), Article e0251008. https://doi.org/10.1371/journal.pone.0251008

• Lam, O. H. Y., Dogotari, M., Prüm, M., Vithlani, H. N., Roers, C., Melville, B., Zimmer, F., & Becker, R. (2021). An open source workflow for weed mapping in native grassland using unmanned aerial vehicle: Using Rumex obtusifolius as a case study. European Journal of Remote Sensing, 54(sup1), 71–88. https://doi.org/10.1080/22797254.2020.1793687

• Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674

• Louargant, M., Villette, S., Jones, G., Vigneau, N., Paoli, J. N., & Gée, C. (2017). Weed detection by UAV: Simulation of the impact of spectral mixing in multispectral images. Precision Agriculture, 18(6), 932–951. https://doi.org/10.1007/s11119-017-9528-3

• Ma, X., Deng, X., Qi, L., Jiang, Y., Li, H., Wang, Y., & Xing, X. (2019). Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields. PLoS ONE, 14(4), Article e0215676. https://doi.org/10.1371/journal.pone.0215676

• Maes, W. H., & Steppe, K. (2019). Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends in Plant Science, 24(2), 152–164. https://doi.org/10.1016/j.tplants.2018.11.007

• Mateen, A. (2019). Weed detection in wheat crop using UAV for precision agriculture. Pakistan Journal of Agricultural Sciences, 56(03), 775–784. https://doi.org/10.21162/pakjas/19.8036

• Mini, G. A., Oliva Sales, D., & Luppe, M. (2020, December 16-18). Weed segmentation in sugarcane crops using Mask R-CNN through aerial images. [Paper presentation]. International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, USA. https://doi.org/10.1109/CSCI51800.2020.00088

• Mink, R., Dutta, A., Peteinatos, G. G., Sökefeld, M., Engels, J. J., Hahn, M., & Gerhards, R. (2018). Multi-temporal site-specific weed control of Cirsium arvense (L.) scop. and Rumex crispus L. in maize and sugar beet using unmanned aerial vehicle based mapping. Agriculture, 8(5), Article 65. https://doi.org/10.3390/agriculture8050065

• Mispan, M. S., Bzoor, M. I., Mahmod, I. F., Md-Akhir, A. H. B., & Zulrushdi, A. Q. (2019). Managing weedy rice (Oryza sativa L.) in Malaysia: Challenges and ways forward. Journal of Research in Weed Science, 2, 149–167. https://doi.org/10.26655/JRWEEDSCI.2019.3.6

• Moazzam, S. I., Khan, U. S., Qureshi, W. S., Nawaz, T., & Kunwar, F. (2023). Towards automated weed detection through two-stage semantic segmentation of tobacco and weed pixels in aerial Imagery. Smart Agricultural Technology, 4, Article 100142. https://doi.org/10.1016/j.atech.2022.100142

• Moazzam, S. I., Khan, U. S., Qureshi, W. S., Tiwana, M. I., Rashid, N., Hamza, A., Kunwar, F., & Nawaz, T. (2022). Patch-wise weed coarse segmentation mask from aerial imagery of sesame crop. Computers and Electronics in Agriculture, 203, Article 107458. https://doi.org/10.1016/j.compag.2022.107458

• Monteiro, A., & Santos, S. (2022). Sustainable approach to weed management: The role of precision weed management. Agronomy, 12(1), Article 118. https://doi.org/10.3390/agronomy12010118

• Motmainna, M., Juraimi, A. S., Uddin, M. K., Asib, N. B., Islam, A. K. M. M., & Hasan, M. (2021a). Allelopathic potential of Malaysian invasive weed species on Weedy rice (Oryza sativa f. spontanea Roshev). Allelopathy Journal, 53, 53-68. https://doi.org/10.26651/allelo.j/2021-53-1-1327

• Motmainna, M., Juraimi, A. S., Uddin, M. K., Asib, N. B., Islam, A. K. M. M., & Hasan, M. (2021b) Bioherbicidal properties of Parthenium hysterophorus, Cleome rutidosperma and Borreria alata extracts on selected crop and weed species. Agronomy, 11(4), Article 643. https://doi.org/10.3390/agronomy11040643

• Motmainna, M., Juraimi, A. S., Uddin, M. K., Asib, N. B., Islam, A. K. M. M., & Hasan, M. (2021c). Assessment of allelopathic compounds to develop new natural herbicides: A review. Allelopathy Journal, 52, 21-40. https://doi.org/10.26651/allelo.j/2021-52-1-1305

• Motmainna, M., Juraimi, A. S., Uddin, M. K., Asib, N. B., Islam, A. K. M. M., Ahmad-Hamdani, M.S., Berahim, Z., & Hasan, M. (2021d). Physiological and Biochemical Responses of Ageratum conyzoides, Oryza sativa f. spontanea (Weedy Rice) and Cyperus iria to Parthenium hysterophorus Methanol Extract. Plants, 10(6), Article 1205. https://doi.org/10.3390/plants10061205

• Motmainna, M., Juraimi, A. S., Uddin, M. K., Asib, N. B., Islam, A. M., Ahmad-Hamdani, M. S., & Hasan, M. (2021e). Phytochemical constituents and allelopathic potential of Parthenium hysterophorus L. in comparison to commercial herbicides to control weeds. Plants, 10(7), Article 1445. https://doi.org/10.3390/plants10071445

• Nagargade, M., Singh, M., & Tyagi, V. (2018). Ecologically sustainable integrated weed management in dry and irrigated direct-seeded rice. Advances in Plants & Agriculture Research, 8(3), 319-331. https://doi.org/10.15406/apar.2018.08.00333

• Nawaz, A., Rehman, A. U., Rehman, A., Ahmad, S., Siddique, K. H. M., & Farooq, M. (2022). Increasing sustainability for rice production systems. Journal of Cereal Science, 103, Article 103400. https://doi.org/10.1016/j.jcs.2021.103400

• Parico, A. I. B., & Ahamed, T. (2020). An aerial weed detection system for green onion crops using the you only look once (YOLOv3) deep learning algorithm. Engineering in Agriculture, Environment and Food, 13(2), 42–48. https://doi.org/10.37221/eaef.13.2_42

• Pei, H., Sun, Y., Huang, H., Zhang, W., Sheng, J., & Zhang, Z. (2022). Weed detection in maize fields by UAV images based on crop row preprocessing and improved YOLOv4. Agriculture, 12(7), Article 975. https://doi.org/10.3390/agriculture12070975

• Pignatti, S., Casa, R., Harfouche, A., Huang, W., Palombo, A., & Pascucci, S. (2019, July 18-August 2). Maize crop and weeds species detection by using UAV VNIR hyperpectral data. [Paper presentation]. International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan. https://doi.org/10.1109/IGARSS.2019.8900241

• Rahman, A. N. M. R. B., & Zhang, J. (2022). Trends in rice research: 2030 and beyond. Food and Energy Security, 12(2), Article e390. https://doi.org/10.1002/fes3.390

• Rahman, M. F. F., Fan, S., Zhang, Y., & Chen, L. (2021). A comparative study on application of unmanned aerial vehicle systems in agriculture. Agriculture, 11(1), Article 22. https://doi.org/10.3390/agriculture11010022

• Razfar, N., True, J., Bassiouny, R., Venkatesh, V., & Kashef, R. (2022). Weed detection in soybean crops using custom lightweight deep learning models. Journal of Agriculture and Food Research, 8, Article 100308. https://doi.org/10.1016/j.jafr.2022.100308

• Reedha, R., Dericquebourg, E., Canals, R., & Hafiane, A. (2022). Transformer neural network for weed and crop classification of high resolution UAV images. Remote Sensing, 14(3), Article 592. https://doi.org/10.3390/rs14030592

• Rosle, R., Sulaiman, N., Che′Ya, N. N., Radzi, M. F. M., Omar, M. H., Berahim, Z., Ilahi, W. F. F., Shah, J. A., & Ismail, M. R. (2022). Weed detection in rice fields using UAV and multispectral aerial imagery. Chemistry Proceedings, 10(1), Article 44. https://doi.org/10.3390/IOCAG2022-12519

• Roslim, M. H. M., Juraimi, A. S., Che’ya, N. N., Sulaiman, N., Manaf, M. N. H. A., Ramli, Z., & Motmainna, M. (2021). Using remote sensing and an unmanned aerial system for weed management in agricultural crops: A review. Agronomy, 11(9), Article 1809. https://doi.org/10.3390/agronomy11091809

• Ruzmi, R., Ahmad-Hamdani, M. S., Abidin, M. Z. Z., & Roma-Burgos, N. (2021). Evolution of imidazolinone-resistant weedy rice in Malaysia: The current status. Weed Science, 69(5), 598–608. https://doi.org/10.1017/wsc.2021.33

• Sa, I., Popović, M., Khanna, R., Chen, Z., Lottes, P., Liebisch, F., Nieto, J., Stachniss, C., Walter, A., & Siegwart, R. (2018). WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sensing, 10(9), Article 1423. https://doi.org/10.3390/rs10091423

• Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2020). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 4843-4873. https://doi.org/ 10.1109/ACCESS.2020.3048415

• Shekhawat, K., Rathore, S. S., & Chauhan, B. S. (2020). Weed management in dry direct-seeded rice: A review on challenges and opportunities for sustainable rice production. Agronomy, 10(9), Article 1264. https://doi.org/10.3390/agronomy10091264

• Sivakumar, A. N. V., Li, J., Scott, S., Psota, E., Jhala, A. J., Luck, J. D., & Shi, Y. (2020). Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery. Remote Sensing, 12(13), Article 2136. https://doi.org/10.3390/rs12132136

• Stroppiana, D., Villa, P., Sona, G., Ronchetti, G., Candiani, G., Pepe, M., Busetto, L., Migliazzi, M., & Boschetti, M. (2018). Early season weed mapping in rice crops using multi-spectral UAV data. International Journal of Remote Sensing, 39(15–16), 5432–5452. https://doi.org/10.1080/01431161.2018.1441569

• Su, J., Yi, D., Coombes, M., Liu, C., Zhai, X., McDonald-Maier, K., & Chen, W. H. (2022). Spectral analysis and mapping of blackgrass weed by leveraging machine learning and UAV multispectral imagery. Computers and Electronics in Agriculture, 192, Article 106621. https://doi.org/10.1016/j.compag.2021.106621

• Sulaiman, N., Norasma, N., Ya, C., Huzaifah, M., Roslim, M., Juraimi, A. S., Noor, N. M., Fazilah, W., & Ilahi, F. (2022). The application of hyperspectral remote sensing imagery (HRSI) for weed detection analysis in rice fields. A review. Applied Sciences, 12(5), Article 2570. https://doi.org/10.3390/app12052570

• Tu, Y. H., Phinn, S., Johansen, K., & Robson, A. (2018). Assessing radiometric correction approaches for multi-spectral UAS imagery for horticultural applications. Remote Sensing, 10(11), Article 1684. https://doi.org/10.3390/rs10111684

• Valente, J., Doldersum, M., Roers, C., & Kooistra, L. (2019). Detecting Rumex obtusifolius weed palnts in grasslands from UAV RGB imagery using deep learning. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 179-185. https://doi.org/10.5194/isprs-annals-IV-2-W5-179-2019

• Wang, S., Han, Y., Chen, J., He, X., Zhang, Z., Liu, X., & Zhang, K. (2022). Weed density extraction based on few-shot learning through UAV remote sensing RGB and multispectral images in ecological irrigation area. Frontiers in Plant Science, 12, Article 735230. https://doi.org/10.3389/fpls.2021.735230

• Zhang, Y., Gao, J., Cen, H., Lu, Y., Yu, X., He, Y., & Pieters, J. G. (2019). Automated spectral feature extraction from hyperspectral images to differentiate weedy rice and barnyard grass from a rice crop. Computers and Electronics in Agriculture, 159, 42–49. https://doi.org/10.1016/j.compag.2019.02.018

• Zou, K., Chen, X., Zhang, F., Zhou, H., & Zhang, C. (2021). A field weed density evaluation method based on uav imaging and modified u-net. Remote Sensing, 13(2), Article 310. https://doi.org/10.3390/rs13020310