PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• 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