PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Using a conventional approach via visual observation on the ground, farmers encounter difficulties monitoring the entire paddy field area, and it is time-consuming to do manually. The application of unmanned aerial vehicles (UAVs) could help farmers optimise inputs such as water and fertiliser to increase yield, productivity, and quality, allowing them to manage their operations at lower costs and with minimum environmental impact. Therefore, this article aims to provide an overview of the integration of UAV and multispectral sensors in monitoring paddy growth applications based on vegetation indices and soil plant analysis development (SPAD) data. The article briefly describes current rice production in Malaysia and a general concept of precision agriculture technologies. The application of multispectral sensors integrated with UAVs in monitoring paddy growth is highlighted. Previous research on aerial imagery derived from the multispectral sensor using the normalised difference vegetation index (NDVI) is explored to provide information regarding the health condition of the paddy. Validation of the paddy growth map using SPAD data in determining the leaf’s relative chlorophyll and nitrogen content is also being discussed. Implementation of precision agriculture among low-income farmers could provide valuable insights into the practical implications of this review. With ongoing education, training and experience, farmers can eventually manage the UAV independently in the field. This article concludes with a future research direction regarding the production of growth maps for other crops using a variety of vegetation indices and map validation using the SPAD metre values.

• Abijo, A., Lee, C.-Y., Huang, C.-Y., Ho, P.-C., & Tsai, K.-J. (2023). The beneficial role of photobiomodulation in neurodegenerative diseases. Biomedicines, 11(7), 1828-1850. https://doi.org/10.3390/biomedicines11071828

• Adnan, N., & Nordin, S. M. (2021). How COVID 19 effect Malaysian paddy industry? Adoption of green fertilizer a potential resolution. Environment, Development and Sustainability, 23, 8089-8129. https://doi.org/10.1007/s10668-020-00978-6

• Akhtar, R., & Masud, M. M. (2022). Dynamic linkages between climatic variables and agriculture production in Malaysia: a generalized method of moments approach. Environmental Science and Pollution Research, 29, 41557-41566. https://doi.org/10.1007/s11356-021-18210-x

• Ali, I., Cawkwell, F., Dwyer, E., Barrett, B., & Green, S. (2016). Satellite remote sensing of grasslands: From observation to management. Journal of Plant Ecology, 9(6), 649-671. https://doi.org/10.1093/jpe/rtw005

• Alou, I. N., Steyn, J. M., Annandale, J. G., & Van der Laan, M. (2018). Growth, phenological, and yield response of upland rice (Oryza sativa L. cv. Nerica 4®) to water stress during different growth stages. Agricultural Water Management, 198, 39-52. https://doi.org/10.1016/j.agwat.2017.12.005

• Barbedo, J. G. A. (2019). Detection of nutrition deficiencies in plants using proximal images and machine learning: A review. Computers and Electronics in Agriculture, 162, 482-492. https://doi.org/10.1016/j.compag.2019.04.035

• Bazezew, M. N., Belay, A. T., Guda, S. T., & Kleinn, C. (2021). Developing maize yield predictive models from sentinel-2 msi derived vegetation indices: an approach to an early warning system on yield fluctuation and food security. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 89, 535-548. https://doi.org/10.1007/s41064-021-00178-5

• Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidisb, G., Wan, S., & Goudos, S. K. (2020). Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18, Article 100187. https://doi.org/10.1016/j.iot.2020.100187

• Bujang, A. S., & Bakar, B. H. A. (2019, August). Precision agriculture in Malaysia. In Proceedings of International Workshop on ICTs for Precision Agriculture (pp. 6-8). ResearchGate.

• Chemura, A., Mutanga, O., & Dube, T. (2017). Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies. International Journal of Applied Earth Observation and Geoinformation, 57, 1-13. https://doi.org/10.1016/j.jag.2016.12.007

• Chen, X., Thorp, K. R., Ouyang, Z., Hou, Y., Zhou, B., & Li, Y. (2019). Energy consumption due to groundwater pumping for irrigation in the North China Plain. Science of The Total Environment, 669, 1033-1042. https://doi.org/10.1016/j.scitotenv.2019.03.179

• Cherlinka, V. (2023). NDVI FAQ: All you need to know about index. EOS Data Analytics. https://eos.com/blog/ndvi-faq-all-you-need-to-know-about-ndvi/

• Chusnah, W. N., Chu, H. J., Tatas, & Jaelani, L. M. (2023). Machine-learning estimation of high spatiotemporal resolution chlorophyll-a concentration using multi-satellite imagery. Sustainable Environment Research, 33(1), Article 11. https://doi.org/10.1186/s42834-023-00170-1

• Corti, M., Cavalli, D., Cabassi, G., Vigoni, A., Degano, L., & Gallina, P. M. (2019). Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables. Precision Agriculture, 20, 675-696. https://doi.org/10.1007/s11119-018-9609-y

• Costa, E. M., Tassinari, W. D. S., Pinheiro, H. S. K., Beutler, S. J., & Dos Anjos, L. H. C. (2018). Mapping soil organic carbon and organic matter fractions by geographically weighted regression. Journal of Environmental Quality, 47(4), 718-725. https://doi.org/10.2134/jeq2017.04.0178

• Dardak, R. A. (2015). Transformation of Agricultural Sector in Malaysia through Agricultural Policy. Malaysian Agricultural Research and Development Institute (MARDI).

• Della Chiesa, T., Del Grosso, S. J., Hartman, M. D., Parton, W. J., Echarte, L., Yahdjian, L., & Piñeiro, G. (2022). A novel mechanism to simulate intercropping and relay cropping using the DayCent model. Ecological Modelling, 465, Article 109869. https://doi.org/10.1016/j.ecolmodel.2021.109869

• Deng, L., Mao, Z., Li, X., Hu, Z., Duan, F., & Yan, Y. (2018). UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 124-136. https://doi.org/10.1016/j.isprsjprs.2018.09.008

• Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), Article 1745. https://doi.org/10.3390/agriculture12101745

• Dhau, I., Adam, E., Mutanga, O., & Ayisi, K. K. (2018). Detecting the severity of maize streak virus infestations in maize crop using in situ hyperspectral data. Transactions of the Royal Society of South Africa, 73(1), 8-15. https://doi.org/10.1080/0035919X.2017.1370034

• Dorairaj, D., & Govender, N. T. (2023). Rice and paddy industry in Malaysia: governance and policies, research trends, technology adoption and resilience. Frontiers in Sustainable Food Systems, 7, Article 1093605. https://doi.org/10.3389/fsufs.2023.1093605

• Duan, B., Fang, S., Zhu, R., Wu, X., Wang, S., Gong, Y., & Peng, Y. (2019) Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis. Frontiers Plant Science, 10, Article 204. https://doi.org/10.3389/fpls.2019.00204

• Elfri, M. A. A., Rahman, F. H., Newaz, S. H., Suhaili, W. S., & Au, T. W. (2023). Determining paddy crop health from aerial image using machine learning approach: A Brunei Darussalam based study. In AIP Conference Proceedings (Vol. 2643, No. 1). AIP Publishing. https://doi.org/10.1063/5.0113668

• Fahmi, Z., Samah, B. A., & Abdullah, H. (2013). Paddy industry and paddy farmers well-being: A success recipe for agriculture industry in Malaysia. Asian Social Science, 9(3), Article 177. https://doi.org/10.5539/ass.v9n3p177

• FAO. (2022). Status of Digital Agriculture in 47 Sub-Saharan African Countries. Food and Agriculture Organization of the United Nations. https://www.fao.org/3/cb7943en/cb7943en.pdf

• Farag, M. A., Sheashea, M., Zhao, C., & Maamoun, A. A. (2022). UV fingerprinting approaches for quality control analyses of food and functional food coupled to chemometrics: A comprehensive analysis of novel trends and applications. Foods, 11(18), Article 2867. https://doi.org/10.3390/foods11182867

• Feng, Z., Song, L., Duan, J., He, L., Zhang, Y., Wei, Y., & Feng, W. (2022). Monitoring wheat powdery mildew based on hyperspectral, thermal infrared, and RGB image data fusion. Sensors, 22(1), Article 31. https://doi.org/10.3390/s22010031

• Fenghua, Y., Tongyu, X., Yingli, C., Guijun, Y., Wen, D., & Shu, W. (2016). Models for estimating the leaf NDVI of japonica rice on a canopy scale by combining canopy NDVI and multisource environmental data in Northeast China. International Journal of Agricultural and Biological Engineering, 9(5), 132-142. https://doi.org/10.3965/j.ijabe.20160905.2266

• Firdaus, R. R., Leong Tan, M., Rahmat, S. R., & Senevi Gunaratne, M. (2020). Paddy, rice and food security in Malaysia: A review of climate change impacts. Cogent Social Sciences, 6(1), Articlw 1818373. https://doi.org/10.1080/23311886.2020.1818373

• Gée, C., Denimal, E., Merienne, J., & Larmure, A. (2021). Evaluation of weed impact on wheat biomass by combining visible imagery with a plant growth model: Towards new non-destructive indicators for weed competition. Precision Agriculture, 22, 550-568. https://doi.org/10.1007/s11119-020-09776-6

• Gohain, G. B., Singh, K. K., Singh, R. S., Dakhore, K. K., & Ghosh, K. (2022). Application of CERES-sorghum crop simulation model DSSAT v4. 7 for determining crop water stress in crop phenological stages. Modeling Earth Systems and Environment, 8, 1963-1975. https://doi.org/10.1007/s40808-021-01194-5

• Gracia-Romero, A., Kefauver, S. C., Vergara-Diaz, O., Zaman-Allah, M. A., Prasanna, B. M., Cairns, J. E., & Araus, J. L. (2017). Comparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization. Frontiers in Plant Science, 8, Article 2004. https://doi.org/10.3389/fpls.2017.02004

• Grzebisz, W., & Łukowiak, R. (2021). Nitrogen gap amelioration is a core for sustainable intensification of agriculture - A concept. Agronomy, 11(3), Article 419. https://doi.org/10.3390/agronomy11030419

• 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

• Guo, Y., Yin, G., Sun, H., Wang, H., Chen, S., Senthilnath, J., Wang, J., & Fu, Y. (2020). Scaling effects on chlorophyll content estimations with RGB camera mounted on a UAV platform using machine-learning methods. Sensors, 20(18), Article 5130. http:// doi.org/10.3390/s20185130

• Hassan, S. I., Alam, M. M., Illahi, U., Al Ghamdi, M. A., Almotiri, S. H., & Su’ud, M. M. (2021). A systematic review on monitoring and advanced control strategies in smart agriculture. IEEE Access, 9, 32517-32548. https://doi.org/10.1109/ACCESS.2021.3057865

• Hassler, S. C., & Baysal-Gurel, F. (2019). Unmanned aircraft system (UAS) technology and applications in agriculture. Agronomy, 9(10), Article 618. https://doi.org/10.3390/agronomy9100618

• Hogan, S., Kelly, N., Stark, B., & Chen, Y. (2017). Unmanned aerial systems for agriculture and natural resources. California Agriculture, 71(1), 5-14. https://doi.org/10.3733/ca.2017a0002

• Hou, W., Tränkner, M., Lu, J., Yan, J., Huang, S., Ren, T., Cong, R., & Li, X. (2020). Diagnosis of nitrogen nutrition in rice leaves influenced by potassium levels. Frontiers in Plant Science, 11, Article 165. https://doi.org/10.3389/fpls.2020.00165

• Ibrahim, E. S., Rufin, P., Nill, L., Kamali, B., Nendel, C., & Hostert, P. (2021). Mapping crop types and cropping systems in Nigeria with sentinel-2 imagery. Remote Sensing, 13(17), Article 3523. https://doi.org/10.3390/rs13173523

• Irmulatov, B. R., Abdullaev, K. K., Komarov, A. A., & Yakushev, V. V. (2021). Prospects for precision management of wheat productivity in the conditions of Northern Kazakhstan. Agricultural Biology, 56(1), 92-102. https://doi.org/10.15389/agrobiology.2021.1.92eng

• Ishihara, M., Inoue, Y., Ono, K., Shimizu, M., & Matsuura, S. (2015). The impact of sunlight conditions on the consistency of vegetation indices in croplands - Effective usage of vegetation indices from continuous ground-based spectral measurements. Remote Sensing, 7(10), 14079-14098. https://doi.org/10.3390/rs71014079

• Iwahashi, Y., Sigit, G., Utoyo, B., Lubis, I., Junaedi, A., Trisasongko, B. H., Wijaya, I. M. A. S., Maki, M., Hongo, C., & Homma, K. (2022). Drought damage assessment for crop insurance based on vegetation index by unmanned aerial vehicle (UAV) multispectral images of paddy fields in Indonesia. Agriculture, 13(1), Article 113. https://doi.org/10.3390/agriculture13010113

• Jamroen, C., Komkum, P., Fongkerd, C., & Krongpha, W. (2020). An intelligent irrigation scheduling system using low-cost wireless sensor network toward sustainable and precision agriculture. IEEE Access, 8, 172756-172769. https://doi.org/10.1109/ACCESS.2020.3025590

• Janga, B., Asamani, G. P., Sun, Z., & Cristea, N. (2023). A review of practical ai for remote sensing in earth sciences. Remote Sensing, 15(16), Article 4112. https://doi.org/10.3390/rs15164112

• Kalischuk, M., Paret, M. L., Freeman, J. H., Raj, D., Da Silva, S., Eubanks, S., Wiggins, D. J., Lollar, M., Marois, J. J., Mellinger, H. C., & Das, J. (2019). An improved crop scouting technique incorporating unmanned aerial vehicle–assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon. Plant Disease, 103(7), 1642-1650. https://doi.org/10.1094/PDIS-08-18-1373-RE

• Kamarianakis, Z., & Panagiotakis, S. (2023). Design and implementation of a low-cost chlorophyll content meter. Sensors, 23(5), Article 2699. https://doi.org/10.3390/s23052699

• Karunanithy, K., & Velusamy, B. (2021). Directional antenna based node localization and reliable data collection mechanism using local sink for wireless sensor networks. Journal of Industrial Information Integration, 24, Article 100222. https://doi.org/10.1016/j.jii.2021.100222

• Kasim, N. M., Ahmad, M. H., Shaharudin, A. B., Naidu, B. M., Chan, Y. Y., & Aris, T. (2018). Food choices among Malaysian adults: Findings from Malaysian adults nutrition survey (MANS) 2003 and MANS 2014. Malaysian Journal of Nutrition, 24(1), 63-75.

• Kazemi, F., & Parmehr, E. G. (2023). Evaluation of RGB vegetation indices derived from UAV images for rice crop growth monitoring. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 385-390. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-385-2023

• Kganyago, M., Ovakoglou, G., Mhangara, P., Adjorlolo, C., Alexandridis, T., Laneve, G., & Beltran, J. S. (2023). Evaluating the contribution of Sentinel-2 view and illumination geometry to the accuracy of retrieving essential crop parameters. GIScience & Remote Sensing, 60(1), Article 2163046. https://doi.org/10.1080/15481603.2022.2163046

• Low, J. W., Ortiz, R., Vandamme, E., Andrade, M., Biazin, B., & Grüneberg, W. J. (2020). Nutrient-dense orange-fleshed sweetpotato: Advances in drought-tolerance breeding and understanding of management practices for sustainable next-generation cropping systems in sub-Saharan Africa. Frontiers in Sustainable Food Systems, 4, Article 50. https://doi.org/https://doi.org/10.3389/fsufs.2020.00050

• Lu, W., Okayama, T., & Komatsuzaki, M. (2021). Rice height monitoring between different estimation models using UAV photogrammetry and multispectral technology. Remote Sensing, 14(1), Article 78. https://doi.org/10.3390/rs14010078

• Luo, S., Jiang, X., Jiao, W., Yang, K., Li, Y., & Fang, S. (2022). Remotely sensed prediction of rice yield at different growth durations using UAV multispectral imagery. Agriculture, 12(9), Article 1447. https://doi.org/10.3390/agriculture12091447

• Mallareddy, M., Thirumalaikumar, R., Balasubramanian, P., Naseeruddin, R., Nithya, N., Mariadoss, A., Eazhilkrishna, N., Choudhary, A. K., Deiveegan, M., Subrramanian, E., Padmaja, B., & Vijayakumar, S. (2023). Maximizing water use efficiency in rice farming: A comprehensive review of innovative irrigation management technologies. Water, 15(10), Article 1802. https://doi.org/10.3390/w15101802

• Mapfumo, R. B., Murwira, A., Masocha, M., & Andriani, R. (2017). Detection of subtle deforestation due to logging using satellite remote sensing in wet and dry savanna woodlands of Southern Africa. Geocarto International, 32(5), 514-530. https://doi.org/10.1080/10106049.2016.1161074

• McCarthy, C., Nyoni, Y., Kachamba, D. J., Banda, L. B., Moyo, B., Chisambi, C., Banfill, J., & Hoshino, B. (2023). Can drones help smallholder farmers improve agriculture efficiencies and reduce food insecurity in Sub-Saharan Africa? Local perceptions from Malawi. Agriculture, 13(5), Article 1075. https://doi.org/10.3390/agriculture13051075

• McCarty, J. L., Neigh, C. S. R., Carroll, M. L., & Wooten, M. R. (2017). Extracting smallholder cropped area in Tigray, Ethiopia with wall-to-wall sub-meter WorldView and moderate resolution Landsat 8 imagery. Remote Sensing of Environment, 202, 142-151. https://doi.org/10.1016/j.rse.2017.06.040

• Meroni, M., d’Andrimont, R., Vrieling, A., Fasbender, D., Lemoine, G., Rembold, F., Seguini, L., & Verhegghen, A. (2021). Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and-2. Remote Sensing of Environment, 253, Article 112232. https://doi.org/10.1016/j.rse.2020.112232

• Ministry of Agiculture. (1984). Third National Agricultural Policy (1998 ‐ 2010) ‐ Executive Summary. https://www.pmo.gov.my/dokumenattached/Dasar/29THIRD_NATIONAL_AGRICULTURAL_POLICY_(1998_-_2010)_-_EXECUTIVE_SUMMARY.pdf

• Ministry of Agriculture (2016). Dasar Sekuriti Makanan Negara [National Food Security Policy]. Ministry of Agriculture and Agro-based Industry. https://www.kpkm.gov.my/

• Monteiro, A., Santos, S., & Gonçalves, P. (2021). Precision agriculture for crop and livestock farming - Brief review. Animals, 11(8), Article 2345. https://doi.org/10.3390/ani11082345

• Montilla, R., Montilla, G., Perez, E., Frassato, L., & Seijas, C. (2021). Precision agriculture for rice crops with an emphasis in low health index areas. Revista Facultad Nacional de Agronomía Medellín, 74(1), 9383-9393. https://doi.org/10.15446/rfnam.v74n1.85310

• Munnaf, M. A., Haesaert, G., Van Meirvenne, M., & Mouazen, A. M. (2020). Site-specific seeding using multi-sensor and data fusion techniques: A review. Advances in Agronomy, 161, 241-323. https://doi.org/10.1016/bs.agron.2019.08.001

• Naguib, N. S., & Daliman, S. (2022, November). Analysis of NDVI and NDRE indices using satellite images for crop identification at Kelantan. In IOP Conference Series: Earth and Environmental Science (Vol. 1102, No. 1, p. 012054). IOP Publishing. https://doi.org/10.1088/1755-1315/1102/1/012054

• Nestel, D., Cohen, Y., Shaked, B., Alchanatis, V., Nemny-Lavy, E., Miranda, M. A., Sciarretta, A., & Papadopoulos, N. T. (2019). An integrated decision support system for environmentally-friendly management of the Ethiopian fruit fly in greenhouse crops. Agronomy, 9(8), Article 459. https://doi.org/10.3390/agronomy9080459

• Neupane, K., & Baysal-Gurel, F. (2021). Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: A review. Remote Sensing, 13(19), Article 3841. https://doi.org/10.3390/rs13193841

• Nguy-Robertson, A., Gitelson, A., Peng, Y., Viña, A., Arkebauer, T., & Rundquist, D. (2012). Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity. Agronomy Journal Abstract – Biometry, Modeling and Statistics, 104(5), 1336-1347. https://doi.org/10.2134/agronj2012.0065

• Norasma, C. Y. N., Fadzilah, M. A., Roslin, N. A., Zanariah, Z. W. N., Tarmidi, Z., & Candra, F. S. (2019, April). Unmanned aerial vehicle applications in agriculture. In IOP Conference Series: Materials Science and Engineering (Vol. 506, p. 012063). IOP Publishing. https://doi.org/10.1088/1757-899X/506/1/012063

• Olson, D., & Anderson, J. (2021). Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal, 113(2), 971-992. https://doi.org/10.1002/agj2.20595

• Omar, S. C., Shaharudin, A., & Tumin, S. A. (2019). The Status of the Paddy and Rice Industry in Malaysia. Khazanah Research Institute. https://www.krinstitute.org/assets/contentMS/img/template/editor/Rice%20Report_Ppt%20Slide_Sarena.pdf

• Onyango, C. M., Nyaga, J. M., Wetterlind, J., Söderström, M., & Piikki, K. (2021). Precision agriculture for resource use efficiency in smallholder farming systems in Sub-Saharan Africa: A systematic review. Sustainability, 13(3), Article 1158. https://doi.org/10.3390/su13031158

• Osman, Z., & Shahiri, H. (2017). Ethnic and gender inequality in employment during the new economic policy. Institutions and Economies, 6(1), 57-72.

• Othman, K., Omar, H., Fuad, H. A., Laidin, J., & Ramli, I. M. (2020). The causal impact of government support on the small strategic crop industry: Malaysia’s experience. Asian Journal of Agriculture and Rural Development, 10(1), 298-310. https://doi.org/10.18488/journal.1005/2020.10.1/1005.1.298.310

• Pérez-Ortiz, M., Gutiérrez, P. A., Peña, J. M., Torres-Sánchez, J., López-Granados, F., & Hervás-Martínez, C. (2016). Machine learning paradigms for weed mapping via unmanned aerial vehicles. In 2016 IEEE symposium series on computational intelligence (SSCI) (pp. 1-8). IEEE Publication. https://doi.org/10.1109/SSCI.2016.7849987

• Pokhrel, A., Virk, S., Snider, J. L., Vellidis, G., Hand, L. C., Sintim, H. Y., Parkash, V., Chalise, D. P., Lee, J. M., & Byers, C. (2023). Estimating yield-contributing physiological parameters of cotton using UAV-based imagery. Frontiers in Plant Science, 14. https://doi.org/10.3389%2Ffpls.2023.1248152

• Ponnusamy, V., & Natarajan, S. (2021). Precision agriculture using advanced technology of IoT, unmanned aerial vehicle, augmented reality, and machine learning. In D. Gupta, C. Hugo, de Albuquerque, A. Khanna & P. L. Mehta, (Eds.), Smart Sensors for Industrial Internet of Things (pp. 207-229). Springer. https://doi.org/10.1007/978-3-030-52624-5_14

• Pretorius, Z. A., Lan, C. X., Prins, R., Knight, V., McLaren, N. W., Singh, R. P., Bender, C. M., & Kloppers, F. J. (2017). Application of remote sensing to identify adult plant resistance loci to stripe rust in two bread wheat mapping populations. Precision Agriculture, 18, 411-428. https://doi.org/10.1007/s11119-016-9461-x

• Raddi, S., Giannetti, F., Martini, S., Farinella, F., Chirici, G., Tani, A., Maltoni, A., & Mariotti, B. (2022). Monitoring drought response and chlorophyll content in Quercus by consumer-grade, near-infrared (NIR) camera: A comparison with reflectance spectroscopy. New Forests, 53(2), 241-265. https://doi.org/10.1007/s11056-021-09848-z

• Rahmat, S. R., Firdaus, R. R., Mohamad Shaharudin, S., & Yee Ling, L. (2019). Leading key players and support system in Malaysian paddy production chain. Cogent Food & Agriculture, 5(1), Article 1708682. https://doi.org/10.1080/23311932.2019.1708682

• Ramli, N. N., Shamsudin, M. N., Mohamed, Z., & Radam, A. (2012). The impact of fertilizer subsidy on Malaysia paddy/rice industry using a system dynamics approach. International Journal of Social Science and Humanity, 2(3), Article 213.

• Richard, K., Abdel-Rahman, E. M., Subramanian, S., Nyasani, J. O., Thiel, M., Jozani, H., Borgemeister, C., & Landmann, T. (2017). Maize cropping systems mapping using rapideye observations in agro-ecological landscapes in Kenya. Sensors, 17(11), Article 2537. https://doi.org/10.3390/s17112537

• Roman, A., & Ursu, T. (2016). Multispectral satellite imagery and airborne laser scanning techniques for the detection of archaeological vegetation marks. In C. H. Opreanu & V. A. Lazarecu (Eds.), Landscape Archaeology on the Northern Frontier of the Roman Empire at Porolissum: An Interdisciplinary Research Project (pp. 141-152). Mega Publishing House.

• Rosle, R., Che’Ya, N. 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. https://doi.org/10.1088/1755-1315/355/1/012066

• 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

• Roth, L., Barendregt, C., Bétrix, C. A., Hund, A., & Walter, A. (2022). High-throughput field phenotyping of soybean: Spotting an ideotype. Remote Sensing of Environment, 269, Article 112797. https://doi.org/10.1016/j.rse.2021.112797

• Rusli, N. M., Noor, Z. Z., & Taib, S. M. (2024). Life cycle assessment of rice production in Muda Granary Area, Kedah, Malaysia. Journal of Advanced Research in Applied Sciences and Engineering Technology, 35(2), 69-83. https://doi.org/10.37934/araset.35.2.6983

• Sari, M. Y. A., Hassim, Y. M. M., Hidayat, R., & Ahmad, A. (2021). Monitoring rice crop and paddy field condition using UAV RGB imagery. International Journal on Informatics Visualization, 5(4), 469-474. https://dx.doi.org/10.30630/joiv.5.4.742

• Sato, N. K., Tsuji, T., Iijima, Y., Sekiya, N., & Watanabe, K. (2023). Predicting rice lodging risk from the distribution of available nitrogen in soil using uas images in a paddy field. Sensors, 23(14), Article 6466. https://doi.org/10.3390/s23146466

• Seglah, P. A., Wang, Y., Wang, H., Bi, Y., Zhou, K., Wang, Y., Wang, Y., Wang, H., & Feng, X. (2020). Crop straw utilization and field burning in Northern region of Ghana. Journal of Cleaner Production, 261, Article 121191. https://doi.org/10.1016/j.jclepro.2020.121191

• Sharabiani, V. R., Nazarloo, A. S., Taghinezhad, E., Veza, I., Szumny, A., & Figiel, A. (2023). Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR. Food Science & Nutrition, 11(5), 2166-2175. https://doi.org/10.1002/fsn3.3071

• Shu, M., Zuo, J., Shen, M., Yin, P., Wang, M., Yang, X., Tang, J., Li, B., & Ma, Y. (2021). Improving the estimation accuracy of SPAD values for maize leaves by removing UAV hyperspectral image backgrounds. International Journal of Remote Sensing, 42, 5862-5881. https://doi.org/10.1080/01431161.2021.1931539

• Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), Article 3136. https://doi.org/10.3390/rs12193136

• Souza, F. H. Q., Martins, P. H. A., Martins, T. H. D., Teodoro, P. E., & Baio, F. H. R. (2020). The use of vegetation index via remote sensing allows estimation of soybean application rate. Remote Sensing Applications: Society and Environment, 17, Article 100279. https://doi.org/10.1016/j.rsase.2019.100279

• Stöcker, C., Bennett, R., Nex, F., Gerke, M., & Zevenbergen, J. (2017). Review of the current state of UAV regulations. Remote Sensing, 9(5), Article 459. https://doi.org/10.3390/rs9050459

• Sudu, B., Rong, G., Guga, S., Li, K., Zhi, F., Guo, Y., Zhang, J., & Bao, Y. (2022). Retrieving SPAD values of summer maize using UAV hyperspectral data based on multiple machine learning algorithm. Remote Sensing, 14(21), Article 5407. https://doi.org/10.3390/rs14215407

• Sui, Y. Y., Wang, Q. Y., & Yu, H. Y. (2016). Prediction of greenhouse cucumber disease based on chlorophyll fluorescence spectrum index. Guang pu xue yu Guang pu fen xi= Guang pu, 36(6), 1779-1782.

• Takoutsing, B., Martín, J. A. R., Weber, J. C., Shepherd, K., Sila, A., & Tondoh, J. (2017). Landscape approach to assess key soil functional properties in the highlands of Cameroon: Repercussions of spatial relationships for land management interventions. Journal of Geochemical Exploration, 178, 35-44. https://doi.org/10.1016/j.gexplo.2017.03.014

• The Star. (2019, April 13). Where does Malaysia’s paddy and rice industry stand? The Star. https://www.thestar.com.my/business/business-news/2019/04/13/where-does-malaysias-paddy-and-rice-industry-stand/

• Tsai, D. M., & Chen, W. L. (2017). Coffee plantation area recognition in satellite images using Fourier transform. Computers and Electronics in Agriculture, 135, 115-127. https://doi.org/10.1016/j.compag.2016.12.020

• Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), Article 349. https://doi.org/10.3390/info10110349

• USDA. (2020). World Agricultural Production. USDA Foreign Agricultural Service, USA. https://www.fas.usda.gov/data/world-agricultural-production

• Wan, L., Cen, H., Zhu, J., Zhang, J., Zhu, Y., Sun, D., Du, X., Zhai, L., Weng, H., Li, Y., Li, X., Bao, Y., Shou, J., & He, Y. (2020). Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer - A case study of small farmlands in the South of China. Agricultural and Forest Meteorology, 291, Article 108096. https://doi.org/10.1016/j.agrformet.2020.108096

• Wan, W., Zhao, Y., Xu, J., Liu, K., Guan, S., Chai, Y., Cui, H., Wu, P., & Diao, M. (2022). Reducing and delaying nitrogen recommended by leaf critical SPAD value was more suitable for nitrogen utilization of spring wheat under a new type of drip-irrigated system. Agronomy, 12(10), Article 2331. https://doi.org/10.3390/agronomy12102331

• Wang, K., Huggins, D. R., & Tao, H. (2019). Rapid mapping of winter wheat yield, protein, and nitrogen uptake using remote and proximal sensing. International Journal of Applied Earth Observation and Geoinformation, 82, Article 101921. https://doi.org/10.1016/j.jag.2019.101921

• Wang, Y. P., Chang, Y. C., & Shen, Y. (2022). Estimation of nitrogen status of paddy rice at vegetative phase using unmanned aerial vehicle based multispectral imagery. Precision Agriculture, 23(1), 1-17. https://doi.org/10.1007/s11119-021-09823-w

• Winowiecki, L. A., Vågen, T. G., Boeckx, P., & Dungait, J. A. (2017). Landscape-scale assessments of stable carbon isotopes in soil under diverse vegetation classes in East Africa: Application of near-infrared spectroscopy. Plant and Soil, 421, 259-272. https://doi.org/10.1007/s11104-017-3418-3

• Xie, C., & Yang, C. (2020). A review on plant high-throughput phenotyping traits using UAV-based sensors. Computers and Electronics in Agriculture, 178, Article 105731. https://doi.org/10.1016/j.compag.2020.105731

• Yang, X., Yang, R., Ye, Y., Yuan, Z., Wang, D., & Hua, K. (2021). Winter wheat SPAD estimation from UAV hyperspectral data using cluster-regression methods. International Journal of Applied Earth Observation and Geoinformation, 105, Article 102618. https://doi.org/10.1016/j.jag.2021.102618

• Yin, Q., Zhang, Y., Li, W., Wang, J., Wang, W., Ahmad, I., Zhou, G., & Huo, Z. (2023). Estimation of winter wheat SPAD values based on UAV multispectral remote sensing. Remote Sensing, 15(14), Article 3595. https://doi.org/10.3390/rs15143595

• Yuan, Z., Cao, Q., Zhang, K., Ata-Ul-Karim, S. T., Tian, Y., Zhu, Y., Cao, W., & Liu, X. (2016). Optimal leaf positions for SPAD meter measurement in rice. Frontiers in plant science, 7, Article 719. https://doi.org/10.3389/fpls.2016.00719

• 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, K., Liu, X., Ma, Y., Wang, Y., Cao, Q., Zhu, Y., Cao, W., & Tian, Y. (2021). A new canopy chlorophyll index-based paddy rice critical nitrogen dilution curve in eastern China. Field Crops Research, 266, Article 108139. https://doi.org/10.1016/j.fcr.2021.108139

• Zhang, R., Yang, P., Liu, S., Wang, C., & Liu, J. (2022). Evaluation of the methods for estimating leaf chlorophyll content with SPAD chlorophyll meters. Remote Sensing, 14(20), Article 5144. https://doi.org/10.3390/rs14205144

• Zhang, S., Zhao, G., Lang, K., Su, B., Chen, X., Xi, X., & Zhang, H. (2019) Integrated satellite, unmanned aerial vehicle (UAV) and ground inversion of the SPAD of winter wheat in the reviving stage. Sensors, 19(17), Article 1485. http://doi.org/10.3390/s19071485

• Zhang, Z., & Zhu, L. (2023). A review on unmanned aerial vehicle remote sensing: platforms, sensors, data processing methods, and applications. Drones, 7(6), Article 398. https://doi.org/10.3390/drones7060398

• Zhao, Y., Yang, P., Cheng, Y., Liu, Y., Yang, Y., & Liu, Z. (2023). Insights into the physiological, molecular, and genetic regulators of albinism in Camellia sinensis leaves. Frontiers in Genetics, 14. https://doi.org/10.3389%2Ffgene.2023.1219335

• Zhu, W., Feng, Z., Dai, S., Zhang, P., & Wei, X. (2022). Using UAV multispectral remote sensing with appropriate spatial resolution and machine learning to monitor wheat scab. Agriculture, 12(11), Article 1785. https://doi.org/10.3390/agriculture12111785