Sedge is one type of weed that can infest the rice field, as well as broadleaf and grasses. If sedges are not appropriately controlled, severe yield loss will occur due to increased competition with cultivated rice for light, space, nutrients, and water. Both sedges and grasses are monocots and have similar narrowed leaf characteristics, but most sedge stems have triangular prismatic shapes in cross sections, which differ them from grasses. Event sedges and grasses differ in morphology, but differentiating them in rice fields is challenging due to the large rice field area and high green color similarity. In addition, climate change makes it more challenging as the distribution of sedge weed infestation is influenced by surrounding abiotic factors, which lead to changes in weed control management. With advanced drone technology, agriculture officers or scientists can save time and labor in distributing weed surveys in rice fields. Using hyperspectral sensors on drones can increase classification accuracy and differentiation between weed species. The spectral signature of sedge weed species captured by the hyperspectral drone can generate weed maps in rice fields to give the sedge percentage distribution and location of sedge patch growth. Researchers can propose proper countermeasures to control the sedge weed problem with this information. This review summarizes the advances in our understanding of the hyperspectral reflectance of weedy sedges in rice fields. It also discusses how they interact with climate change and phenological stages to predict sedge invasions.

• Agrawal, S., & Das, M. L. (2011, December 8-10). Internet of things - A paradigm shift of future Internet applications. [Paper presentation]. Nirma University International Conference on Engineering, Ahmedabad, India. https://doi.org/10.1109/NUiConE.2011.6153246

• Alam, M., Siwar, C., Talib, B., & Toriman, M. (2014). Impacts of climatic changes on paddy production in Malaysia: Micro study on IADA at North West Selangor. Research Journal of Environmental and Earth Sciences, 6(5), 251-258.

• Anwar, P., Juraimi, A. S., Puteh, A., Selamat, A., Man, A., & Hakim, A. (2011). Seeding method and rate influence on weed suppression in aerobic rice. African Journal of Biotechnology, 10(68), 15259-15271. https://doi.org/10.5897/AJB11.060

• Aqilah, A., Asyraf, M., & Azmi, M. (2012, November 22-24). Weed survey in different cultural practice in Seberang Perai and Muda Rice Fields in Northern Malaysia. [Paper presentation]. Proceedings of the 2nd Annual International Conference, Syiah Kuala University & The 8th IMT-GT Uninet Biosciences Conference, Aceh, Indonesia.

• Arias, F., Zambrano, M., Broce, K., Medina, C., Pacheco, H., & Nunez, Y. (2021). Hyperspectral imaging for rice cultivation: Applications, methods and challenges. AIMS Agriculture and Food, 6(1), 273-307. https://doi.org/10.3934/agrfood.2021018

• Azmi, M., Abdullah, M. Z., Mislamah, B., & Baki, B. B. (2000). Management of Weedy Rice (Oryza sativa L.): The Malaysian Experience. ReseachGate.

• Basinger, N. T., Jennings, K. M., Hestir, E. L., Monks, D. W., Jordan, D. L., & Everman, W. J. (2020). Phenology affects differentiation of crop and weed species using hyperspectral remote sensing. Weed Technology, 34(6), 897-908. https://doi.org/10.1017/wet.2020.92

• Begum, M., Juraimi, A. S., Amartalingam, R., Man, A., & Rastans, S. O. S. (2006). The effects of sowing depth and flooding on the emergence, survival, and growth of Fimbristylis miliacea (L.) Vahl. Weed Biology and Management, 6(3), 157-164. https://doi.org/10.1111/j.1445-6664.2006.00209.x

• Bell, M. A., Fischer, R. A., Byerlee, D., & Sayre, K. (1995). Genetic and agronomic contributions to yield gains: A case study for wheat. Field Crops Research, 44(2-3), 55-65. https://doi.org/10.1016/0378-4290(95)00049-6

• Bhoi, S. K., Jena, K. K., Panda, S. K., Long, H. V., Kumar, R., Subbulakshmi, P., & Jebreen, H. B. (2021). An internet of things assisted unmanned aerial vehicle based artificial intelligence model for rice pest detection. Microprocessors and Microsystems, 80, Article 103607. https://doi.org/10.1016/j.micpro.2020.103607

• Bruhl, J. J., & Wilson, K. L. (2007). Towards a comprehensive survey of C3 and C4 photosynthetic pathways in Cyperaceae. Aliso: A Journal of Systematic and Floristic Botany, 23(1), 99-148. https://doi.org/10.5642/aliso.20072301.11

• Caton, B. P., Mortimer, M., Hill, J. E., Johnson, D. E. (2010). A practical field guide to weeds of rice in Asia. International Rice Research Institute. https://ageconsearch.umn.edu/record/164472

• Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61-69. https://doi.org/10.1016/j.compag.2018.05.012

• Dankhara, F., Patel, K., & Doshi, N. (2019). Analysis of robust weed detection techniques based on the internet of things (IoT). Procedia Computer Science, 160, 696-701. https://doi.org/10.1016/j.procs.2019.11.025

• Dilipkumar, M., Chuah, T. S., Goh, S. S., & Sahid, I. (2020). Weed management issues, challenges, and opportunities in Malaysia. Crop Protection, 134, Article 104347. https://doi.org/10.1016/j.cropro.2017.08.027

• 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

• Gabriel, B. P., Hamda, M., Thamrin, M., Budiman, A., Asikin, S., & Badaruddin, H. (1986, August 24-34). Pest management of food crops in the tidal and monotonous swamps of South Kalimantan. [Paper presentation]. Symposium on Lowland Development in Indonesia, Jakarta, Indonesia.

• Galal, T. M., & Shehata, H. S. (2015). Impact of nutrients and heavy metals capture by weeds on the growth and production of rice (Oryza sativa L.) irrigated with different water sources. Ecological Indicators, 54, 108-115. https://doi.org/10.1016/j.ecolind.2015.02.024

• Glaroudis, D., Iossifides, A., & Chatzimisios, P. (2020). Survey, comparison and research challenges of IoT application protocols for smart farming. Computer Networks, 168, Article 107037. https://doi.org/10.1016/j.comnet.2019.107037

• Govaerts, R., & Simpson, D. A. (2007). World Checklist of Cyperaceae. Kew Pub., Royal Botanic Gardens.

• Hakim, M. A., Juraimi, A. S., Hanafi, M. M., Ismail, M. R., & Selamat, A. (2013). A comparison of weed communities of coastal rice fields in Peninsular Malaysia. Journal of Environmental Biology, 34(5), Article 847.

• Hakim, M. A., Juraimi, A. S., Hanafi, M. M., Ismail, M. R., Selamat, A., Rafii, M. Y., & Latif, M. A. (2014). Biochemical and anatomical changes and yield reduction in rice (Oryza sativa L.) under varied salinity regimes. BioMed Research International, 2014, Article 208584. https://doi.org/10.1155/2014/208584

• Hakim, M. A., Juraimi, A. S., Ismail, M. R., Hanafi, M. M., & Selamat, A. (2010). Distribution of weed population in the costal rice growing area of Kedah in peninsular Malaysia. Journal of Agronomy 9(1), 9-16. https://doi.org/10.3923/ja.2010.9.16

• 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

• Iqbal, M. A., Ali, S., Sabagh, A. E., Ahmad, Z., & Siddiqui, M. H. (2020). Changing climate and advances on weeds utilization as forage: Provisions, nutritional quality and implications. In H. El-Shafie (Ed.) Invasive Species - Introduction Pathways, Economic Impact, and Possible Management Options (pp. 1-13). IntechOpen. https://doi.org/10.5772/intechopen.91386

• Islam, N., Rashid, M. M., Pasandideh, F., Ray, B., Moore, S., & Kadel, R. (2021). A review of applications and communication technologies for internet of things (IoT) and unmanned aerial vehicle (UAV) based sustainable smart farming. Sustainability, 13(4), Article 1821. https://doi.org/10.3390/su13041821

• Ismail, B. S., & Siddique, A. B. (2012). Allelopathic inhibition by Fimbristylis miliacea on the growth of the rice plants. Advances in Environmental Biology, 6(8), 2423-2428.

• Ismail, S. N. F., & Abdullah, A. S. (2020). Recent developments of weed management in rice fields. Reviews in Agricultural Science, 8, 343-353. https://doi.org/10.7831/ras.8.0_343

• Issahaku, A., Francis, K. O., & Richard, W. Y. (2021). Capabilities gained by rice farmers from JICA’s training on sustainable rain-fed lowland rice production technology in Northern, Savanna and North-east. ADRRI Journal (Multidisciplinary), 30(2 (7), 33-52.

• Jinger, D., Kaur, R., Kaur, N., Rajanna, G. A., Kumari, K., & Dass, A. (2017). Weed dynamics under changing climate scenario: A Review. International Journal of Current Microbiology and Applied Sciences, 6(3), 2376-2388. https://doi.org/10.20546/ijcmas.2017.603.272

• Juraimi, A. S., Begum, M., Mohd. Yusof, M. N., & Man, A. (2010). Efficacy of herbicides on the control weeds and productivity of direct seeded rice under minimal water conditions. Plant Protection Quarterly, 25(1), 19-25.

• Juraimi, A. S., Najib, M. M., Begum, M., Anuar, A. R., Azmi, M., & Puteh, A. (2009). Critical period of weed competition in direct seeded rice under saturated and flooded conditions. Pertanika Journal of Tropical Agricultural Science, 32(2), 305-316.

• Juraimi, A. S., Uddin, M. K., Anwar, M. P., Mohamed, M. T. M., Ismail, M. R., & Man, A. (2013). Sustainable weed management in direct seeded rice culture: A review. Australian Journal of Crop Science, 7(7), 989-1002.

• Khush, G. S. (1997). Origin, dispersal, cultivation and variation of rice. Plant Molecular Biology, 35, 25-34. https://doi.org/10.1023/A:1005810616885

• Kurniadie, D., Irda, M., Umiyati, U., Widayat, D., & Nasahi, C. (2019). Weeds diversity of lowland rice (Oryza sativa L.) with different farming system in Purwakarta Regency Indonesia. Journal of Agronomy, 18(1), 21-26. https://doi.org/10.3923/ja.2019.21.26

• Lausch, A., Salbach, C., Schmidt, A., Doktor, D., Merbach, I., & Pause, M. (2015). Deriving phenology of barley with imaging hyperspectral remote sensing. Ecological Modelling, 295, 123-135. https://doi.org/10.1016/j.ecolmodel.2014.10.001

• Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote Sensing and Image Interpretation. John Wiley & Sons.

• Man, A., Mohammad Saad, M., Amzah, B., Masarudin, M. F., Jack, A., Misman, S. N., & Ramachandran, K. (2018). Buku Poket Perosak, Penyakit dan Rumpai Padi di Malaysia [Pocket Book of Rice Pests, Diseases and Weeds in Malaysia] (Cetakan Kelima). Institut Penyelidikan dan Kemajuan Pertanian Malaysia (MARDI).

• Mondal, M. M. A., Hakim, M. A., Juraimi, A. S., & Azad, M. A. K. (2011). Contribution of morpho-physiological attributes in determining the yield of mungbean. African Journal of Biotechnology, 10(60), 12897-12904. https://doi.org/10.5897/AJB11.373

• 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). 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. M., Ahmad-Hamdani, M. S., & Hasan, M. (2021c). 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

• 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. K. M. M., & Hasan, M. (2021e) 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

• Norasma, C. Y. N., Alahyadi, L. A. N., Fazilah, F. F. W., Roslan, S. N. A., & Tarmidi, Z. (2020). Identification spectral signature of weed species in rice using spectroradiometer handheld sensor. IOP Conference Series: Earth and Environmental Science, 540, Article 012091. doi:10.1088/1755-1315/540/1/012091

• Pantazi, X. E., Moshou, D., & Bravo, C. (2016). Active learning system for weed species recognition based on hyperspectral sensing. Biosystems Engineering, 146, 193-202. https://doi.org/10.1016/j.biosystemseng.2016.01.014

• Punalekar, S., Verhoef, A., Tatarenko, I. V., Van der Tol, C., Macdonald, D. M., Marchant, B., Gerard, F., White, K., & Gowing, D. (2016). Characterization of a highly biodiverse floodplain meadow using hyperspectral remote sensing within a plant functional trait framework. Remote Sensing, 8(2), Article 112. https://doi.org/10.3390/rs8020112

• Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, Article 107148. https://doi.org/10.1016/j.comnet.2020.107148

• Rahim, F. H. A., Hawari, N. N., & Abidin, N. Z. (2017). Supply and demand of rice in Malaysia: A system dynamics approach. International Journal of Supply Chain Management, 6(4), 1-7.

• Ramesh, K., Matloob, A., Aslam, F., Florentine, S. K., & Chauhan, B. S. (2017). Weeds in a changing climate: Vulnerabilities, consequences, and implications for future weed management. Frontiers in Plant Science, 8, Article 95. https://doi.org/10.3389/fpls.2017.00095

• Rodenburg, J., Meinke, H., & Johnson, D. E. (2011). Challenges for weed management in African rice systems in a changing climate. The Journal of Agricultural Science, 149(4), 427-435. https://doi.org/10.1017/S0021859611000207

• Roslin, N. A., Che’Ya, N. N., Sulaiman, N., Alahyadi, L. A. N., & Ismail, M. R. (2021). Mobile application development for spectral signature of weed species in rice farming. Pertanika Journal of Science & Technology, 29(4), 2241-2259. https://doi.org/10.47836/pjst.29.4.01

• Shahzadi, R., Ferzund, J., Tausif, M., & Suryani, M. A. (2016). Internet of things based expert system for smart agriculture. International Journal of Advanced Computer Science and Applications, 7(9), 341-350.

• Simma, B., Polthanee, A., Goggi, A. S., Siri, B., Promkhambut, A., & Caragea, P. C. (2017). Wood vinegar seed priming improves yield and suppresses weeds in dryland direct-seeding rice under rainfed production. Agronomy for Sustainable Development, 37, Article 56. https://doi.org/10.1007/s13593-017-0466-2

• Simpson, D. A., Yesson, C., Culham, A., Couch, C. A., & Muasya, A. M. (2011). Climate change and Cyperaceae. In T. R. Hodkinson, M. B. Jones, S. Waldren & J. A. N. Parnell (Eds.), Climate Change, Ecology and Systematics (pp. 439-456). Cambridge University Press.

• Stuecker, M. F., Tigchelaar, M., & Kantar, M. B. (2018). Climate variability impacts on rice production in the Philippines. PloS One, 13(8), Article e0201426. https://doi.org/10.1371/journal.pone.0201426

• Su, W. H. (2020). Advanced machine learning in point spectroscopy, RGB-and hyperspectral-imaging for automatic discriminations of crops and weeds: A review. Smart Cities, 3(3), 767-792. https://doi.org/10.3390/smartcities3030039

• Torres-Sánchez, J., López-Granados, F., & Pena, J. M. (2015). An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, 114, 43-52. https://doi.org/10.1016/j.compag.2015.03.019

• Uddin, M. K., Juraimi, A. S., Ismail, M. R., & Brosnan, J. T. (2010). Characterizing weed populations in different turfgrass sites throughout the Klang Valley of Western Peninsular Malaysia. Weed Technology, 24(2), 173-181. https://doi.org/10.1614/WT-09-046.1

• Ueno, O. (2001). Environmental regulation of C3 and C4 differentiation in the amphibious sedge eleocharis vivipara. Plant Physiology, 127(4), 1524-1532. https://doi.org/10.1104/pp.010704

• Varanasi, A., Prasad, P. V., & Jugulam, M. (2016). Impact of climate change factors on weeds and herbicide efficacy. Advances in Agronomy, 135, 107-146. https://doi.org/10.1016/bs.agron.2015.09.002

• Voss, H. M. G., Escobar, O. D. S., Trivisiol, V. S., Peripolli, M., Pivetta, M., Rubert, J., Nunes, E. A., & Dornelles, S. H. B. (2021). Phenology and thermal requirements of the species Cyperus difformis L. in southern Brazil. Acta Scientiarum. Biological Sciences, 43, e47560-e47560. https://doi.org/10.4025/actascibiolsci.v43i1.47560

• Xie, X., Li, Y. X., Li, R., Zhang, Y., Huo, Y., Bao, Y., & Shen, S. (2013). Hyperspectral characteristics and growth monitoring of rice (Oryza sativa) under asymmetric warming. International Journal of Remote Sensing, 34(23), 8449-8462. https://doi.org/10.1080/01431161.2013.843806

• Yaduraju, N. T., & Mishra, J. S. (2008). Sedges in rice culture and their management. In Y. Singh, V. P. Singh, B. Chauhan, A. Orr, A. M. Mortimer, D. E. Johnson & B. Hardy (Eds.), Direct Seeding of Rice and Weed Management in the Irrigated Rice-Wheat Cropping System of the Indo-Gangetic Plains (pp. 191-203). Philippines International Rice Research Institute.

• 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