Precision Farming Techniques for Sustainable Rice Production – A Review
Abstract
Rice (Oryza sativa L.) is the primary staple food for a large proportion of the global population, particularly in Asia, where it accounts for nearly 90% of global rice production and consumption. In India, rice has been cultivated on about 46 million hectares. However, productivity and resource-use efficiency remain sub-optimal due to conventional practices characterised by excessive use of fertilisers, water, pesticides and labour, resulting in increased production costs and environmental degradation. Nitrogen use efficiency in rice rarely exceeds 30–40% under blanket fertiliser recommendations, leading to significant nutrient losses and pollution. Precision farming-based site-specific management offers a sustainable alternative by integrating data-driven tools for nutrient, water, weed and crop management. Site-specific nutrient management using tools such as Leaf Colour Chart, SPAD meter, Green Seeker, Nutrient Expert and Rice Crop Manager synchronises nutrient supply with crop demand, reducing nitrogen use by 15–30% while improving grain yield by 10–25%. Precision water management practices, including alternate wetting and drying, sensor-based irrigation and micro irrigation systems, achieve 20–40% savings in irrigation water without yield penalties, while improving water productivity and reducing methane emissions. Precision weed and disease management using sensors, remote sensing and uncrewed aerial vehicles enables early detection and site-specific interventions, resulting in 40–45% reductions in pesticide use with stable yields. Precision mechanisation technologies, such as laser land levelling, mechanised establishment, and autonomous harvesting, further enhance crop uniformity and operational efficiency. Despite adoption constraints related to cost, technical complexity, awareness, and fragmented landholdings, this review highlights precision farming as a sustainable pathway to enhance the productivity, profitability, and environmental sustainability of rice-based systems.
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Introduction
Rice (Oryza sativa L.) is a vital food crop worldwide and a staple for more than half of the global population, with Asia as the dominant rice-consuming region. It supplies nearly one-fifth of the global dietary energy and plays an indispensable role in food security, livelihood support and poverty reduction, particularly among small and marginal farming communities (FAO, 2020; Sun et al., 2022). Asia accounts for nearly 90% of global rice production and consumption. At present, India is the world's largest rice producer, cultivating approximately 47 million hectares and producing nearly 158.5 million tonnes of paddy annually, with an average productivity of about 2.9 t ha⁻¹ (GOI, 2023; FAO, 2023). Despite this vast cultivation area, rice yields in several regions remain well below attainable levels due to inefficient resource use, climate-related stresses and escalating input costs. Consequently, achieving sustainable rice production has emerged as a significant global concern.
Traditional rice cultivation systems are predominantly based on continuous flooding, uniform fertiliser schedules, blanket pesticide applications and intensive reliance on manual labour. Such practices have led to inefficient utilization of water, nutrients and energy, resulting in environmental degradation and increased production costs. Flooded rice cultivation requires enormous volumes of water, often ranging from 3000 to 5000 litres to produce one kilogram of grain, which accelerates groundwater depletion and intensifies competition among agricultural, industrial, and domestic water users (IRRI, 2001; Kumar & Ladha, 2011). In addition, poor irrigation management contributes to 40–50% of nonproductive water losses through runoff and percolation, while prolonged flooding enhances methane emissions, raising concerns about the environmental sustainability of rice ecosystems (Belder et al., 2005; Wu et al., 2022).
Efficient nutrient management, particularly nitrogen (N), remains a critical factor influencing rice productivity. Nitrogen is the most limiting nutrient in rice cultivation and is essential for photosynthesis, vegetative growth, tiller formation and grain development (Yoshida et al., 2006; Djaman et al., 2018). However, nitrogen use efficiency in rice systems is typically low, seldom exceeding 30–40%, owing to significant losses through volatilisation, leaching, runoff, and denitrification (Cassman et al., 1998; Nachimuthu et al., 2007). The widespread adoption of blanket fertiliser recommendations fails to address field-level variability in soil fertility and crop demand, often leading to excessive fertiliser use, increased production costs, lodging, pest outbreaks and environmental pollution (Rahman et al., 2007; Djaman et al., 2018). These limitations highlight the importance of adopting need-based and site-specific nutrient management strategies.
In recent years, precision nutrient management tools such as the Leaf Colour Chart (LCC), SPAD chlorophyll meter, Green Seeker and Site-Specific Nutrient Management (SSNM) approaches have gained considerable attention. LCC and SPAD offer rapid, non-destructive and cost-effective methods for monitoring crop nitrogen status under field conditions, enabling better synchronisation between nitrogen supply and crop demand (Peng et al., 1993; Ghosh et al., 2016). Decision-support tools such as Nutrient Expert and Rice Crop Manager (RCM), based on SSNM principles, provide field-specific fertiliser recommendations that have been shown to improve nitrogen use efficiency by 20–30%, enhance grain yield and reduce nutrient losses and greenhouse gas emissions compared with conventional farmer practices (Gupta et al., 2016; Banayo et al., 2018).
Conclusion
Precision farming offers a sustainable pathway to enhance rice productivity while addressing challenges of water scarcity, low nutrient-use efficiency, labour shortages and environmental degradation. Technologies such as LCC, SPAD, Green Seeker, SSNM tools, AWD irrigation, laser land levelling, UAV-based spraying and robotics enable site-specific, data-driven management of water, nutrients and crop health. These approaches improve yield, resource-use efficiency, profitability and environmental sustainability compared with conventional blanket practices. Although constraints such as high initial costs and technical limitations exist, supportive policies, capacity building and digital infrastructure can accelerate adoption. Overall, precision agriculture represents a transformative strategy for resilient and climate-smart rice production systems.
References
- Agustina, H., Jaelani, L. M., & Sanjaya, H. (2024). Estimation of nitrogen content of rice crops using Sentinel-2 data. The Indonesian Journal of Geography, 56(3), 387–395.
- Banayo, N. P. M. C., Haefele, S. M., Desamero, N. V., & Kato, Y. (2018). On-farm assessment of site-specific nutrient management for rainfed lowland rice in the Philippines. Field Crops Research, 220, 88–96.
- Belder, P., Spiertz, J. H. J., Bouman, B. A. M., Lu, G., & Tuong, T. P. (2005). Nitrogen economy and water productivity of lowland rice under water-saving irrigation. Field Crops Research, 93, 169–185.
- Billa, S. K., Murthy, K., Ramana, A. V., & Jagannadham, J. (2020). The potential of green seeker in nitrogen management of transplanted rice crop under north coastal environment of Andhra Pradesh, India. International Journal of Agricultural and Statistics Sciences, 16, 1925–1929.
- Cassman, K. G., Peng, S., Olk, D. C., Ladha, J. K., Reichardt, W., Dobermann, A., & Singh, U. (1998). Opportunities for increased nitrogen-use efficiency from improved resource management in irrigated rice systems. Field Crops Research, 56, 7–39.
- Chaudhary, A., Mishra, A. K., Venkatramanan, V., & Sharma, S. (2025). Enhancing yield and GHG mitigation through site-specific nutrient management for transplanted and direct-seeded rice in Odisha, India. Frontiers in Sustainable Food Systems, 9, Article 1571263.
- Chauhan, B. S., Mahajan, G., Randhawa, R. K., Singh, H., & Kang, M. S. (2014). Global warming and its possible impact on agriculture in India. Advances in Agronomy, 123, 65–121.
- Djaman, K., Mel, V. C., Ametonou, F. Y., El-Namaky, R., Diallo, M. D., & Koudahe, K. (2018). Effect of nitrogen fertilizer dose and application timing on yield and nitrogen use efficiency of irrigated hybrid rice. Journal of Agricultural Science and Food Research, 9(2), Article 223.
- Eid, A. R., Mohamed, M. H., Pipars, S. K., & Bakry, B. A. (2014). Impact of laser land leveling on water productivity of wheat. Current Research in Agricultural Sciences, 1(2), 53–64.
- Elsoragaby, S., Yahya, A., Mahadi, M. R., Mat Nawi, N., & Mairghany, M. (2019). Comparative field performances between conventional combine and mid-size combine in wetland rice cultivation. Heliyon, 5(4), e01427.
- Food and Agriculture Organization of the United Nations. (2020a). FAOSTAT statistical database. FAO.
- Food and Agriculture Organization of the United Nations. (2020b). The state of food and agriculture 2020: Overcoming water challenges in agriculture. FAO.
- Food and Agriculture Organization of the United Nations. (2023). Rice market monitor. FAO.
- Farooq, M., Siddique, K. H. M., Rehman, H., Aziz, T., Lee, D. J., & Wahid, A. (2011). Rice direct seeding: Experiences, challenges and opportunities. Soil and Tillage Research, 111, 87–98.
- Feng, L., Bouman, B. A. M., Tuong, T. P., Cabangon, R. J., Li, Y., Lu, G., & Feng, Y. (2007). Exploring options to grow rice using less water in northern China. Agricultural Water Management, 88(1-3), 23–33.
- Ghosh, M., Kiran, N., Sharma, R. P., & Gupta, S. K. (2016). Need-based nitrogen management using SPAD meter in wheat of eastern India. International Journal for Tropical Agriculture, 34(3), 789–795.
- Goud, B. R., Reddy, G. P., Chandrika, V., Naidu, M. V. S., Sudhakar, P., Reddy, K. M., & Sagar, G. K. (2022). Effect of drip irrigation regimes and nitrogen levels on growth, yield and economics of aerobic rice (Oryza sativa L.). *ORYZA-An International Journal on Rice, 59*(2), 211–220.
- Government of India. (2023). Agricultural statistics at a glance 2023. Ministry of Agriculture and Farmers Welfare, Department of Agriculture and Farmers Welfare.
- Guo, Z., Cai, D., Zhou, Y., Xu, T., & Yu, F. (2024). Identifying rice field weeds from unmanned aerial vehicle remote sensing imagery using deep learning. Plant Methods, 20(1), Article 105.
- Gupta, G., Shrestha, A., Shrestha, S., & Raut, N. (2016). Evaluation of different nutrient management practices on growth and yield of rice. Advances in Plants and Agriculture Research, 3(6), 187–191.
- Guru, P. K., Chhuneja, N. K., Dixit, A., Tiwari, P., & Kumar, A. (2018). Mechanical transplanting of rice in India: Status and future thrust. Oryza, 55(1), 100–106.
- Huang, Y., Hoffmann, W. C., Lan, Y., Wu, W., & Fritz, B. K. (2021). Development of spray systems for UAV application in precision agriculture. Biosystems Engineering, 197, 170–184.
- International Rice Research Institute. (2001). Annual report 2000–01: Rice research – The way forward. IRRI.
- Jat, M. L., Gupta, R., Saharawat, Y. S., & Khosla, R. (2006). Laser land leveling: A precursor technology for resource conservation (Rice-Wheat Consortium Technical Bulletin Series No. 7). New Delhi.
- Jeevan, N., Pazhanivelan, S., & Kumaraperumal, R. (2024). Impact of different herbicide spray fluids applied using drones on nutrient removal by weeds in transplanted rice (Oryza sativa L.). International Journal of Research in Agronomy, 7(9), 5–9.
- Ju, J., Chen, G., Lv, Z., Zhao, M., Sun, L., Wang, Z., & Wang, J. (2024). Design and experiment of an adaptive cruise weeding robot for paddy fields based on improved YOLOv5. Computers and Electronics in Agriculture, 219(19), Article 108824.
- Karthik, R., Ghosh, M., Chowdhury, A. R., & Dhaker, D. L. (2023). Impact of SPAD chlorophyll meter–based nitrogen management strategy on N uptake and soil properties in direct-seeded rice. Oryza: An International Journal on Rice, 60(3), 457–463.
- Keerthi, A. K., Rajakumar, G. R., Jagadeesh, B. R., Priya, P., & Diwan, J. R. (2025). Assessing different SSNM techniques to recommend optimum fertilizer dose under transplanted rice (Oryza sativa L.). International Journal of Research in Agronomy, 8(2), 135–140.
- Krishna, U., Reddy, S. P., & Palkuru, M. (2025). Integration of UAV remote sensing and variable-rate spraying in efficient rice blast control. Journal of Experimental Agriculture International, 47(10), 109–118.
- Kumar, A., Singh, S. K., Kaushal, K. K., & Purushottam, P. (2015). Effect of micro-irrigation on water productivity in system of rice (Oryza sativa L.) and wheat (Triticum aestivum L.) intensification. The Indian Journal of Agricultural Sciences, 85(10), 1342–1348.
- Kumar, R. M. (2023). IoT-enabled AWD irrigation system for reducing GHG emissions and enhancing sustainability of rice production (Technology report). ICAR-Indian Institute of Rice Research, in collaboration with Farms 2 Fork Technologies Pvt. Ltd.
- Kumar, V., & Ladha, J. K. (2011). Direct seeding of rice: Recent developments and future research needs. Advances in Agronomy, 111, 297–413.
- Kumar, V., Singh, S., Sagar, V., & Maurya, M. L. (2018). Evaluation of different crop establishment methods of rice. Journal of Pharmacognosy and Phytochemistry, 7(3), 2295–2298.
- Lee, K., Choi, H., & Kim, J. (2023). Development of path generation and algorithm for autonomous combine harvester using dual GPS antenna. Sensors, 23(10), Article 4944.
- Mahajan, G., Chauhan, B. S., Timsina, J., Singh, P. P., & Singh, K. (2012). Crop performance and water- and nitrogen-use efficiencies in dry-seeded rice. Field Crops Research, 134, 59–70.
- Mahlein, A. K. (2016). Plant disease detection by imaging sensors – Parallels and specific demands for precision agriculture. Plant Disease, 100, 241–251.
- Majumder, S., Shankar, T., Maitra, S., Kumar, A., Gudade, B., Sagar, L., & Dash, S. (2024). Effect of nutrient omission plot technique based nutrient management in rabi rice (Oryza sativa) on crop productivity, nutrient uptake and soil health. Indian Journal of Agronomy, 69(4), 357–363.
- Nachimuthu, G., Velu, V., Malarvizhi, P., Ramasamy, S., & Bose Jaykumar. (2007). Relationship between index leaf nitrogen and leaf colour chart values in direct wet-seeded rice (Oryza sativa L.). Asian Journal of Plant Sciences, 6(3), 477–483.
- Padhan, S. R., Rathore, S. S., Prasad, S. M., Shekhawat, K., & Singh, V. K. (2021). Influence of precision nutrient and weed management on growth and productivity of direct-seeded upland rice (Oryza sativa) under Eastern Plateau and Hills Region. Indian Journal of Agronomy, 66(3), 366–369.
- Palakuru, M., Adamala, S., & Bachina, H. B. (2020). Modeling rice yield using satellite-derived biophysical variables and artificial neural networks. Journal of Agrometeorology, 22(1), 41–47.
- Peng, S., García, F. V., Laza, R. C., & Cassman, K. G. (1993). Adjustment for specific leaf weight improves chlorophyll meter's estimate of rice leaf nitrogen concentration. Agronomy Journal, 85, 987–990.
- Rahman, M. H., Ali, M. H., Ali, M. M., & Khatun, M. M. (2007). Effect of different levels of nitrogen on growth and yield of transplanted Aman rice. International Journal of Sustainable Crop Production, 2(1), 28–34.
- Ramya, R., Reddy, M. D., & Krishna, V. G. (2021). Performance of mechanized transplanting in rice. Indian Journal of Agricultural Sciences, 91, 1123–1128.
- Ramya, S. M. S., Mahesh, N., Revathi, P., & Raju, B. (2021). Effect of laser land levelling and establishment methods on growth and yield of rice. Biological Forum – An International Journal, 13(3a), 73–78.
- Sahoo, S., Mohapatra, B. K., Paikaray, R. K., Jena, S. N., Rath, B. S., Satapathy, M., Panda, R. K., Mishra, K. N., Das, N., Patra, B., & Jena, R. (2022). Impact of site-specific nitrogen management on growth parameters, productivity and profitability of kharif rice. International Journal of Environment and Climate Change, 12(12), 1921–1930.
- Singh, A. K., & Chakraborti, M. (2019). Water and nitrogen use efficiency in SRI through AWD and LCC. The Indian Journal of Agricultural Sciences, 89(12), 2059–2063.
- Sireesha, A., Sreenivas, C., Usha Rani, T., & Satyanarayana, P. V. (2022). Site specific nutrient management through NutriExpert in rice (Oryza sativa L.). Journal of Research, 59(2), 455–458.
- Srivastava, R. K., Singh, D. K., & Pandey, R. (2016). Effect of soil test crop response (STCR) based targeted yield fertilizer strategy on grain and straw yield of rice (Oryza sativa L.). Indian Journal of Agronomy, 61(3), 331–336.
- Sudha Rani, Y., & Jayasree, G. (2014). Mapping of nutrient status of rice soils in Guntur district (Andhra Pradesh) using GIS techniques. The Andhra Agricultural Journal, 61(1), 124–129.
- Sun, C., Zhang, H., Xu, L., Guo, Y., Jiang, J., & Zuo, L. (2022). A 20 m annual paddy rice map for mainland Southeast Asia using Sentinel-1 SAR data. Earth System Science Data, 14, 1–25.
- Tan, X., Shao, D., Liu, H., Yang, F., Xiao, C., & Yang, H. (2013). Effects of alternate wetting and drying irrigation on percolation and nitrogen leaching in paddy fields. Paddy and Water Environment, 11, 381–395.
- Venkatesh Babu, D., Sudhakar, P., Srikanth, B., Srinivasa Reddy, M., & Barghava Rami Reddy, C. (2022). Effect of different levels of nitrogen on SPAD readings and yield in rice (Oryza sativa L.) varieties. The Andhra Agricultural Journal, 69(2), 270–276.
- Wu, K., Li, W., Wei, Z., Dong, Z., Meng, Y., Lv, N., & Zhang, L. (2022a). Effects of mild alternate wetting and drying irrigation and rice straw application on N₂O emissions in rice cultivation. SOIL, 8, 645–654.
- Wu, K., Murayama, S., Nishimura, S., Wang, M., & Sun, Y. (2022b). Effects of mild alternate wetting and drying irrigation and rice straw application on N₂O emissions in rice cultivation. Journal of Soils and Sediments, 22, 1–12.
- Yoshida, H., Horie, T., & Shiraiwa, T. (2006). A model explaining genotypic and environmental variation of rice spikelet number per unit area. Field Crops Research, 97, 337–343.
- Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13, 693–712.
- Zhou, L., Ma, R., Yang, X., Liu, Y., He, Z., Liu, Z., & Xu, L. (2023). Improved yield prediction of ratoon rice using unmanned aerial vehicle-based multi-temporal feature method. Rice Science, 30(3), 247–256.
Zhang, H., Xu, H., Zhu, M., Zeng, M., Zhao, X., & Wu, H. (2025). Research on precision paddy field irrigation control technology based on multi-source sensing hybrid model. Digital Intelligence in Agriculture, 1(1), 14–23.