PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Abdulridha, J., Ehsani, R., & De Castro, A. (2016). Detection and differentiation between laurel wilt disease, phytophthora disease, and salinity damage using a hyperspectral sensing technique. Agriculture, 6(4), Article 56. https://doi.org/10.3390/agriculture6040056

• Adam, S. N. B. (2012). Design and development of an interactive digital spectral library [Unpublished MSc dissertation]. Universiti Putra Malaysia, Malaysia.

• Adebayo, S., Ogunti, E. O., Akingbade, F. K., & Oladimeji, O. (2018). A review of decision support system using mobile applications in the provision of day to day information about farm status for improved crop yield. Periodicals of Engineering and Natural Sciences, 6(2), 89-99. http://dx.doi.org/10.21533/pen.v6i2.183

• Adesina, A. A., Johnson, D. E., & Heinrichs, E. A. (1994). Rice pests in the Ivory Coast, West Africa: Farmers’ perceptions and management strategies. International Journal of Pest Management, 40(4), 293-299. https://doi.org/10.1080/09670879409371902

• Alam, M. M., Siwar, C., Toriman, M. E., Molla, R. I., & Talib, B. (2012). Climate change induced adaptation by paddy farmers in Malaysia. Mitigation and Adaptation Strategies for Global Change, 17(2), 173-186. https://doi.org/10.1007/s11027-011-9319-5

• ASDi. (2014). Handheld 2: Hand-held VNIR spectroradiometer. FieldSpec. Retrieved September 26, 2015, from http://www.asdi.com/products/fieldspec-spectroradiometres /handheld-2- portable-spectroradiometer

• Athirah, R. N., Norasma, C. Y. N., & Ismail, M. R. (2020). Development of an android application for smart farming in crop management. In IOP Conference Series: Earth and Environmental Science (Vol. 540, No. 1, p. 012074). IOP Publishing. https://doi.org/10.1088/1755-1315/540/1/012074

• Bajwa, A. A., Mahajan, G., & Chauhan, B. S. (2015). Nonconventional weed management strategies for modern agriculture. Weed Science, 63(4), 723-747. https://doi.org/10.1614/WS-D-15-00064.1

• Barrero, O., & Perdomo, S. A. (2018). RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Precision Agriculture, 19(5), 809-822. https://doi.org/10.1007/s11119-017-9558-x

• Chen, S. S., Fang, L. G., Liu, Q. H., Chen, L. F., & Tong, Q. X. (2005). The design and development of spectral library of featured crops of South China. In Proceedings 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS’05. (Vol. 2, pp. 4-pp). IEEE Publishing. https://doi.org/10.1109/IGARSS.2005.1525234

• Dela Cruz, G. B. (2019). Nitrogen deficiency mobile application for rice plant through image processing techniques. International Journal of Engineering and Advanced Technology, 8(6), 2950-2955. https://doi.org/10.35940/ijeat.F8721.088619

• Desrial, & Indriawardhana, P. A. K. (2019). Design of online application for agricultural machinery service based on android operating system. In IOP Conference Series: Materials Science and Engineering (Vol. 557, No. 1, p. 012023). IOP Publishing. https://doi.org/10.1088/1757-899x/557/1/012023

• Dilipkumar, M., Burgos, N. R., Chuah, T. S., & Ismail, S. (2018). Cross-resistance to imazapic and imazapyr in a weedy rice (Oryza sativa) biotype found in Malaysia. Planta Daninha, v36, Article e018182239. https://doi.org/10.1590/S0100-83582018360100058

• Haug, S., Michaels, A., Biber, P., & Ostermann, J. (2014). Plant classification system for crop/weed discrimination without segmentation. In IEEE Winter Conference on Applications of Computer Vision (pp. 1142-1149). IEEE Publishing. https://doi.org/10.1109/WACV.2014.6835733

• Henson, Y., Martin, R., Quinnell, R., Van Ogtrop, F., Try, Y., & Tan, D. (2017, September 24-28). Development of a weed identifier mobile application for Cambodian rice farmers. In Proceedings of the 18th Australian Society of Agronomy Conference (pp. 1-4). Ballarat, Australia.

• Ishak, W. W., Hudzari, R. M., & Tan, M. Y. (2013). Development of an automation and control design system for lowland tropical greenhouses. Pertanika Journal of Science & Technology, 21(2), 365-374.

• Jabran, K., Uludag, A., & Chauhan, B. S. (2018). Sustainable weed control in rice. In Weed Control (pp. 276-287). CRC Press.

• Jensen, J. R. (2015). Introductory digital image processing: A remote sensing perspective. Prentice Hall Press.

• Jusoff, K., Yusoff, M. M., & Ali, N. H. M. (2010). Spectral signatures of leaf fall diseases in Hevea brasiliensis using a handheld spectroradiometer. Modern Applied Science, 4(2), 78-84.

• Karim, R. S., Man, A. B., & Sahid, I. B. (2004). Weed problems and their management in rice fields of Malaysia: An overview. Weed Biology and Management, 4(4), 177-186. https://doi.org/10.1111/j.1445-6664.2004.00136.x

• Kokaly, R. F., Clark, R. N., Swayze, G. A., Livo, K. E., Hoefen, T. M., Pearson, N. C., Wise, R. A., Benzel, W. M., Lowers, H. A., Driscoll, R. L., & Klein, A. J. (2017). USGS spectral library version 7 data: US geological survey data release. United States Geological Survey (USGS).

• Labrada, R. (2003). The need for improved weed management in rice. In Proceedings of the 20th Session of the International Rice Commission (pp. 181-189). FAO Publishing.

• Lau, A. M. S., & Hashim, M. (2007). The design and building of spectral library of tropical rain forest in Malaysia. In The 28th Asian Conference on Remote Sensing 2007 (Vol. 2, pp. 1150-1157). Asian Association on Remote Sensing.

• Lin, C. Y., Chang, S. J., Lai, M. H., & Lu, H. Y. (2019, August 6-8). Overview of precision agriculture with focus on rice farming. In International Workshop on ICTs For Precision Agriculture (pp. 19-26). Selangor, Malaysia.

• Liu, T., Chen, W., Wang, Y., Wu, W., Sun, C., Ding, J., & Guo, W. (2017). Rice and wheat grain counting method and software development based on Android system. Computers and Electronics in Agriculture, 141, 302-309. https://doi.org/10.1016/j.compag.2017.08.011

• Lutfi, A. N. A. (2020). Mobile application development for spectral signature of weed species in rice (Degree Thesis). Univerisiti Putra Malaysia, Malaysia.

• Man, A., & Zain, A. M. (1998). Manual for the identification and control of padi angin (weedy rice) in Malaysia. Malaysian Agricultural Research and Development Institute.

• Matloob, A., Khaliq, A., & Chauhan, B. S. (2015). Weeds of direct-seeded rice in Asia: problems and opportunities. Advances in Agronomy, 130, 291-336. https://doi.org/10.1016/bs.agron.2014.10.003

• Medlin, C. R., Shaw, D. R., Gerard, P. D., & LaMastus, F. E. (2000). Using remote sensing to detect weed infestations in Glycine max. Weed Science, 48(3), 393-398. https://doi.org/10.1614/0043-1745(2000)048[0393:URSTDW]2.0.CO;2

• Norasma, C. Y. N. (2016). Site-specific weed management using remote sensing (PhD Thesis). The University of Queensland, Australia.

• 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. In IOP Conference Series: Earth and Environmental Science (Vol. 540, No. 1, p. 012091). IOP Publishing. https://doi.org/10.1088/1755-1315/540/1/012091

• Pongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015). Applications of smartphone-based sensors in agriculture: A systematic review of research. Journal of Sensors, 2015, Article 195308. https://doi.org/10.1155/2015/195308

• Price, J. C. (1994). How unique are spectral signatures? Remote Sensing of Environment, 49(3), 181-186. https://doi.org/10.1016/0034-4257(94)90013-2

• Rahman, M., Blackwell, B., Banerjee, N., & Saraswat, D. (2015). Smartphone-based hierarchical crowdsourcing for weed identification. Computers and Electronics in Agriculture, 113, 14-23. https://doi.org/10.1016/j.compag.2014.12.012

• Ramli, N. S., Hassan, M. S., Man, N., Samah, B. A., Omar, S. Z., Rahman, N. A. A., Yusuf, S., & Ibrahim, M. S. (2019). Seeking of agriculture information through mobile phone among paddy farmers in Selangor. International Journal of Academic Research in Business and Social Sciences, 9(6), 527-538. http://dx.doi.org/10.6007/IJARBSS/v9-i6/5969

• Rao, N. R. (2008). Development of a crop‐specific spectral library and discrimination of various agricultural crop varieties using hyperspectral imagery. International Journal of Remote Sensing, 29(1), 131-144. https://doi.org/10.1080/01431160701241779

• Razali, M. H., Ismail, W. I. W., Ramli, A. R., Sulaiman, M. N., & Harun, M. H. (2009). Development of image based modeling for determination of oil content and days estimation for harvesting of fresh fruit bunches. International Journal of Food Engineering, 5(2), Article 12. https://doi.org/10.2202/1556-3758.1633

• Roslan, S., Razali, M. H. H., Ismail, W. I. W., Abbas, Z., & Zainuddin, M. F. (2013). Rapid detection techniques for mechanical properties determination on surface of Dioscorea hispida rhizome. Procedia Engineering, 68, 446-452. https://doi.org/10.1016/j.proeng.2013.12.205

• Rosle, R., Norasma, C. Y. N., Roslin, N. A., Halip, R. M., & Ismail, M. R. (2019). Monitoring early stage of rice crops growth using normalized difference vegetation index generated from UAV. In IOP Conference Series: Earth and Environmental Science (Vol. 355, No. 1, p. 012066). IOP Publishing.

• Rossel, R. V., Behrens, T., Ben-Dor, E., Brown, D. J., Demattê, J. A. M., Shepherd, K. D., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aichi, H., Barthes, B. G., Bartholomeus, H. M., Bayer, A. D., Bernoux, M., Bottcher, K., Brodsky, L., Du, C. W., Chappell, A., … & Ji, W. (2016). A global spectral library to characterize the world’s soil. Earth-Science Reviews, 155, 198-230. https://doi.org/10.1016/j.earscirev.2016.01.012

• Ruzmi, R., Ahmad‐Hamdani, M. S., & Bakar, B. B. (2017). Prevalence of herbicide‐resistant weed species in Malaysian rice fields: A review. Weed Biology and Management, 17(1), 3-16. https://doi.org/10.1111/wbm.12112

• 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

• Sudianto, E., Neik, T. X., Tam, S. M., Chuah, T. S., Idris, A. A., Olsen, K. M., & Song, B. K. (2016). Morphology of Malaysian weedy rice (Oryza sativa): Diversity, origin and implications for weed management. Weed Science, 64(3), 501-512. https://doi.org/10.1614/WS-D-15-00168.1

• Tang, J. L., Chen, X. Q., Miao, R. H., & Wang, D. (2016). Weed detection using image processing under different illumination for site-specific areas spraying. Computers and Electronics in Agriculture, 122, 103-111. https://doi.org/10.1016/j.compag.2015.12.016

• Vaghefi, N., Shamsudin, M. N., Radam, A., & Rahim, K. A. (2016). Impact of climate change on food security in Malaysia: economic and policy adjustments for rice industry. Journal of Integrative Environmental Sciences, 13(1), 19-35. https://doi.org/10.1080/1943815X.2015.1112292

• Vigueira, C. C., Qi, X., Song, B. K., Li, L. F., Caicedo, A. L., Jia, Y., & Olsen, K. M. (2019). Call of the wild rice: Oryza rufipogon shapes weedy rice evolution in Southeast Asia. Evolutionary applications, 12(1), 93-104. https://doi.org/10.1111/eva.12581

• Wendel, A., & Underwood, J. (2016). Self-supervised weed detection in vegetable crops using ground based hyperspectral imaging. In 2016 IEEE international conference on robotics and automation (ICRA) (pp. 5128-5135). IEEE Publishing. https://doi.org/10.1109/ICRA.2016.7487717

• Yang, X. F., & Kong, C. H. (2017). Interference of allelopathic rice with paddy weeds at the root level. Plant Biology, 19(4), 584-591. https://doi.org/10.1111/plb.12557

• Yuhao, A., Che’Ya, N. N., Roslin, N. A., & Ismail, M. R. (2020). Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery. Pertanika Journal of Science & Technology, 28(3), 779-795.

• Zhang, D., Wang, D., Du, Z., Huang, L., Zhao, H., Liang, D., Gu, C., & Yang, X. (2019). A rapidly diagnosis and application system of fusarium head blight based on smartphone. In 2019 8th International Conference on Agro-Geoinformatics (Agro- Geoinformatics) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820529