Smart Farming for Sustainable Rice Production: An Insight into Application, Challenge, and Future Prospect
Received date: 2023-05-01
Accepted date: 2023-08-04
Online published: 2024-02-06
Rice has a huge impact on socio-economic growth, and ensuring its sustainability and optimal utilization is vital. This review provides an insight into the role of smart farming in enhancing rice productivity. The applications of smart farming in rice production including yield estimation, smart irrigation systems, monitoring disease and growth, and predicting rice quality and classifications are highlighted. The challenges of smart farming in sustainable rice production to enhance the understanding of researchers, policymakers, and stakeholders are discussed. Numerous efforts have been exerted to combat the issues in rice production in order to promote rice sector development. The effective implementation of smart farming in rice production has been facilitated by various technical advancements, particularly the integration of the Internet of Things and artificial intelligence. The future prospects of smart farming in transforming existing rice production practices are also elucidated. Through the utilization of smart farming, the rice industry can attain sustainable and resilient production systems that could mitigate environmental impact and safeguard food security. Thus, the rice industry holds a bright future in transforming current rice production practices into a new outlook in rice smart farming development.
Key words： rice production; smart farming; food security; agriculture sustainability
Norhashila Hashim, Maimunah Mohd Ali, Muhammad Razif Mahadi, Ahmad Fikri Abdullah, Aimrun Wayayok, Muhamad Saufi Mohd Kassim, Askiah Jamaluddin . Smart Farming for Sustainable Rice Production: An Insight into Application, Challenge, and Future Prospect[J]. Rice Science, 2024 , 31(1) : 47 -61 . DOI: 10.1016/j.rsci.2023.08.004
| [1] | Abera B B, Terefe B, Baye K, Covic N. 2019. Rice contribution to food and nutrition security and leveraging opportunities for sustainability, nutrition and health outcomes. In: Ferranti P, Berry E M, Anderson J R. Encyclopedia of Food Security and Sustainability. Amsterdam, the Netherland: Elsevier: 257-263. |
| [2] | Adjao R T, Staatz J M. 2015. Asian rice economy changes and implications for sub-Saharan Africa. Glob Food Secur, 5: 50-55. |
| [3] | Afzal H, Kasi M K. 2019. Ontology-based knowledge modeling for rice crop production. In: 7th International Conference on Future Internet of Things and Cloud (FiCloud). August 26-28, 2019. Istanbul, Turkey: 343-350. |
| [4] | Alfred R, Obit J H, Chin C P Y, Haviluddin H, Lim Y. 2021. Towards paddy rice smart farming: A review on big data, machine learning, and rice production tasks. IEEE Access, 9: 50358-50380. |
| [5] | Ali Chandio A, Gokmenoglu K K, Ahmad M, Jiang Y S. 2022. Towards sustainable rice production in Asia: The role of climatic factors. Earth Syst Environ, 6(1): 1-14. |
| [6] | Almaleeh A A, Zakaria A, Kamarudin L M, Rahiman M H F, Ndzi D L, Ismail I. 2022. Inline 3D volumetric measurement of moisture content in rice using regression-based ML of RF tomographic imaging. Sensors, 22(1): 405. |
| [7] | Arboleda E R, Dizon A A. 2022. Seed of rice plant classification using coarse tree classifier. IAES Int J Robot Autom, 11(2): 727-735. |
| [8] | Asif Saleem M, Aamir M, Ibrahim R, Senan N, Alyas T. 2022. An optimized convolution neural network architecture for paddy disease classification. CMC-Comput Mat Contin, 71(3): 6053-6067. |
| [9] | Aulia I, Wijaya D R, Hidayat W. 2021. Rice quality detection using gradient tree boosting based on electronic nose dataset. In: 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). April 28-30, 2021. Bandung, Indonesia: 1-5. |
| [10] | Azmi N, Kamarudin L M, Zakaria A, Ndzi D L, Rahiman M H F, Zakaria S M M S, Mohamed L. 2021. RF-based moisture content determination in rice using machine learning techniques. Sensors, 21(5): 1875. |
| [11] | Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. 2022. Rapid assessment of rice quality traits using low-cost digital technologies. Foods, 11(9): 1181. |
| [12] | Bacco M, Berton A, Ferro E, Gennaro C, Gotta A, Matteoli S, Paonessa F, Ruggeri M, Virone G, Zanella A. 2018. Smart farming: Opportunities, challenges and technology enablers. In: 2018 IoT Vertical and Topical Summit on Agriculture-Tuscany (IOT Tuscany). May 8-9, 2018. Tuscany, Italy: 1-6. |
| [13] | Bamurigire P, Vodacek A, Jayavel K, Ngoga S R. 2021. A decision-making module for fertilization and irrigation control system in rice farming using markov chain process and SARSA algorithms. In: 2021 11th International Workshop on Computer Science and Engineering, WCSE. June 19-21, 2021. DOI: 10.18178/wcse.2021.06.052. |
| [14] | Devkota K P, Khanda C M, Beebout S J, Mohapatra B K, Singleton G R, Puskur R. 2020. Assessing alternative crop establishment methods with a sustainability lens in rice production systems of Eastern India. J Clean Prod, 244: 118835. |
| [15] | Elders A, Carroll M L, Neigh C S R, D’Agostino A L, Ksoll C, Wooten M R, Brown M E. 2022. Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2. Remote Sens Appl, 27: 100820. |
| [16] | Erlangga F, Wijaya D R, Wikusna W. 2021. Electronic nose dataset for classifying rice quality using neural network. In: 2021 9th International Conference on Information and Communication Technology (ICoICT). August 3-5, 2021. Yogyakarta, Indonesia: IEEE: 462-466. |
| [17] | ESG (Earth Security Group). 2019. Financing Sustainable Rice for a Secure Future: Innovative Finance Partnerships for Climate Mitigation and Adaptation. Essex, United Kingdom: © Earth Security Partnerships 2019: 1-56. |
| [18] | FAO. 2014. A regional rice strategy for sustainable food security in Asia and the Pacific. Rome, Italy: Food and Agriculture Organization of the United Nations Regional Office for Asia and the Pacific Bangkok: 1-73. |
| [19] | FAOSTAT. 2023. Production/yield quantities of rice in World (2016-2022). (2023-12-27) [2024-01-17]. https://www.fao.org/faostat/en/#data/QCL/visualize. |
| [20] | Garnaik S, Samant P K, Mandal M, Mohanty T R, Dwibedi S K, Patra R K, Mohapatra K K, Wanjari R H, Sethi D, Sena D R, Sapkota T B, Nayak J, Patra S, Parihar C M, Nayak H S. 2022. Untangling the effect of soil quality on rice productivity under a 16-years long-term fertilizer experiment using conditional random forest. Comput Electron Agric, 197: 106965. |
| [21] | Geollegue K W V, Arboleda E R, Dizon A A. 2022. Seed of rice plant classification using coarse tree classifier. IAES Int J Artif Intell, 11(2): 727-735. |
| [22] | Giri D, Dhital M, Chaudhary B, Pandey R, Bastakoti B, Shrestha S. 2022. Effect of different nitrogen levels on yield and yield attributes of different rice varieties in DDSR condition at Kanchanpur, Nepal. Arch Agric Environ Sci, 7(3): 310-317. |
| [23] | González Perea R, Camacho Poyato E, Montesinos P, Rodríguez Díaz J A. 2018. Prediction of applied irrigation depths at farm level using artificial intelligence techniques. Agric Water Manag, 206: 229-240. |
| [24] | González Perea R, Camacho Poyato E, Rodríguez Díaz J A. 2021. Forecasting of applied irrigation depths at farm level for energy tariff periods using Coactive neuro-genetic fuzzy system. Agric Water Manag, 256: 107068. |
| [25] | Habib-ur-Rahman M, Ahmad A, Raza A, Hasnain M U, Alharby H F, Alzahrani Y M, Bamagoos A A, Hakeem K R, Ahmad S, Nasim W, Ali S, Mansour F, EI Sabagh A. 2022. Impact of climate change on agricultural production: Issues, challenges, and opportunities in Asia. Front Plant Sci, 13: 925548. |
| [26] | Hama A, Tanaka K, Mochizuki A, Tsuruoka Y, Kondoh A. 2020. Improving the UAV-based yield estimation of paddy rice by using the solar radiation of geostationary satellite Himawari-8. Hydrol Res Lett, 14(1): 56-61. |
| [27] | Hama A, Tanaka K, Chen B, Kondoh A. 2021. Examination of appropriate observation time and correction of vegetation index for drone-based crop monitoring. J Agric Meteorol, 77(3): 200-209. |
| [28] | Hanif S, Wijaya D R, Wikusna W. 2021. K-nearest neighbors algorithm for prediction shelf life of rice based on electronic nose dataset. In: 2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob). April 8-10, 2021. Bandung, Indonesia: IEEE: 116-120. |
| [29] | Jeyaraj P R, Asokan S P, Samuel Nadar E R. 2022. Computer-assisted real-time rice variety learning using deep learning network. Rice Sci, 29(5): 489-498. |
| [30] | Jin B C, Zhang C, Jia L Q, Tang Q Z, Gao L, Zhao G W, Qi H N. 2022. Identification of rice seed varieties based on near-infrared hyperspectral imaging technology combined with deep learning. ACS Omega, 7(6): 4735-4749. |
| [31] | Kadiyala M D M, Jones J W, Mylavarapu R S, Li Y C, Reddy M D. 2015. Identifying irrigation and nitrogen best management practices for aerobic rice-maize cropping system for semi-arid tropics using CERES-rice and maize models. Agric Water Manag, 149: 23-32. |
| [32] | Koklu M, Cinar I, Taspinar Y S. 2021. Classification of rice varieties with deep learning methods. Comput Electron Agric, 187: 106285. |
| [33] | Kusbandhini O J, Wijaya D R, Hidayat W. 2021. Rice shelf-life prediction using support vector regression algorithm based on electronic nose dataset. In: International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). April 28-30, 2021. Bandung, Indonesia: 1-5. |
| [34] | Latif G, Abdelhamid S E, Mallouhy R E, Alghazo J, Kazimi Z A. 2022. Deep learning utilization in agriculture: Detection of rice plant diseases using an improved CNN model. Plants, 11(17): 2230. |
| [35] | Lee K T, Han J, Kim K H. 2022. Optimizing artificial neural network-based models to predict rice blast epidemics in Korea. Plant Pathol J, 38(4): 395-402. |
| [36] | Li D P, Nanseki T, Chomei Y, Kuang J S. 2023. A review of smart agriculture and production practices in Japanese large-scale rice farming. J Sci Food Agric, 103(4): 1609-1620. |
| [37] | Li Y, Xiao J H, Chen L L, Huang X H, Cheng Z K, Han B, Zhang Q F, Wu C Y. 2018. Rice functional genomics research: Past decade and future. Mol Plant, 11(3): 359-380. |
| [38] | Lin S D, Yao Y, Li J Y, Li X B, Ma J, Weng H Y, Cheng Z X, Ye D P. 2023. Application of UAV-based imaging and deep learning in assessment of rice blast resistance. Rice Sci, 30(6): 652-660. |
| [39] | Liu S J, Hu W W, Xu S P, Wu Y J, Liu Y, Wang Y S. 2021. Research and design of intelligent irrigation control system based on fuzzy. In: 4th International Conference on Computer and Communication Engineering Technology (CCET). August 13-15, 2021. Beijing, China: 396-401. |
| [40] | Lu J S, Eitel J U H, Jennewein J S, Zhu J E, Zheng H B, Yao X A, Cheng T, Zhu Y, Cao W X, Tian Y C. 2021. Combining remote sensing and meteorological data for improved rice plant potassium content estimation. Remote Sens, 13(17): 3502. |
| [41] | Mohidem N A, Hashim N, Shamsudin R, Che Man H. 2022. Rice for food security: Revisiting its production, diversity, rice milling process and nutrient content. Agriculture, 12(6): 741. |
| [42] | Moses K, Miglani A, Kumar Kankar P. 2022. Deep CNN-based damage classification of milled rice grains using a high-magnification image dataset. Comput Electron Agric, 195: 106811. |
| [43] | Muthayya S, Sugimoto J D, Montgomery S, Maberly G F. 2014. An overview of global rice production, supply, trade, and consumption. Ann N Y Acad Sci, 1324: 7-14. |
| [44] | Muthusinghe M R S, Palliyaguru S T, Weerakkody W A N D, Hashini Saranga A M, Rankothge W H. 2019. Towards smart farming: Accurate prediction of paddy harvest and rice demand. In: IEEE Region 10 Humanitarian Technology Conference (R10-HTC). December 6-8, 2018. Malambe, Sri Lanka: 1-6. |
| [45] | Najim M M M, Lee T S, Haque M A, Esham M. 2007. Sustainability of rice production: A Malaysian perspective. J Agric Sci, 3(1): 1-12. |
| [46] | Nodin M N, Mustafa Z, Hussain S I. 2022. Assessing rice production efficiency for food security policy planning in Malaysia: A non-parametric bootstrap data envelopment analysis approach. Food Policy, 107: 102208. |
| [47] | Ogunti E O, Akingbade F K, Segun A, Oladimeji O. 2018. Decision support system using mobile applications in the provision of day to day information about farm status to improve crop yield. Period Eng Nat Sci, 6(2): 89-99. |
| [48] | Ojo T O, Ogundeji A A, Emenike C U. 2022. Does adoption of climate change adaptation strategy improve food security? A case of rice farmers in ogun state, Nigeria Land, 11(11): 1875. |
| [49] | Peng H X, Li Z H, Zhou Z Y, Shao Y Y. 2022. Weed detection in paddy field using an improved RetinaNet network. Comput Electron Agric, 199: 107179. |
| [50] | Pham V B, Diep T T, Fock K, Nguyen T S. 2021. Using the Internet of Things to promote alternate wetting and drying irrigation for rice in Vietnam’s Mekong Delta. Agron Sustain Dev, 41(3): 43. |
| [51] | Rahimzadeh H, Sadeghi M, Ahmad Mireei S, Ghasemi-Varnamkhasti M. 2022. Unsupervised modelling of rice aroma change during ageing based on electronic nose coupled with bio-inspired algorithms. Biosyst Eng, 216: 132-146. |
| [52] | Ramesh S, Tamilselvi M, Ramkumar G, Anitha G, Nirmala P. 2022. Comparison and analysis of rice blast disease identification in greenhouse controlled environment and field environment using ML Algorithms. In: International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). January 28-29, 2022. Chennai, India: 1-5. |
| [53] | Rashid A, Mirza S A, Keating C, Ijaz U Z, Ali S, Campos L C. 2022. Machine learning approach to predict quality parameters for bacterial consortium-treated hospital wastewater and phytotoxicity assessment on radish, cauliflower, hot pepper, rice and wheat crops. Water, 14(1): 116. |
| [54] | Rodenburg J, Saito K. 2022. Towards sustainable productivity enhancement of rice-based farming systems in sub-Saharan Africa. Field Crops Res, 287: 108670. |
| [55] | Rowshon M K, Amin M S M. 2010. GIS-based irrigation water management for precision farming of rice. Int J Agric Biol Eng, 3(3): 27-35. |
| [56] | Rowshon M K, Dlamini N S, Mojid M A, Adib M N M, Amin M S M, Lai S H. 2019. Modeling climate-smart decision support system (CSDSS) for analyzing water demand of a large-scale rice irrigation scheme. Agric Water Manag, 216: 138-152. |
| [57] | Ruslan R, Khairunniza-Bejo S, Jahari M, Ibrahim M F. 2022. Weedy rice classification using image processing and a machine learning approach. Agriculture, 12(5): 645. |
| [58] | Saha I, Durand-Morat A, Nalley L L, Alam M J, Nayga R. 2021. Rice quality and its impacts on food security and sustainability in Bangladesh. PLoS One, 16(12): e0261118. |
| [59] | Sai K K, Satyanarayana P, Hussain M A, Suman M. 2019. A real time precision monitoring and detection of rice plant diseases by using internet of things (IoT) based robotics approach. Int J Innov Technol Explor Eng, 8(7): 403-408. |
| [60] | Selvaraj S K, Sanjeev Kumar R, Balamurugan K, Joany R M, Dorothy R, Nguyen T A, Rajendran S. 2022. Wireless nanosensor network for irrigation control. In: Nanosensors for Smart Agriculture. Amsterdam, the Netherland: Elsevier: 463-478. |
| [61] | Shahidur Harun Rumy S M, Arefin Hossain M I, Jahan F, Tanvin T. 2021. An IoT based system with edge intelligence for rice leaf disease detection using machine learning. In: IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). April 21-24, 2021. Ontario, Canada: 1-6. |
| [62] | Shrivastava V K, Pradhan M K, Thakur M P. 2021. Application of pre-trained deep convolutional neural networks for rice plant disease classification. In: International Conference on Artificial Intelligence and Smart Systems (ICAIS). March 25-27, 2021. Coimbatore, India: 1023-1030. |
| [63] | Singh A K, Chourasia B, Raghuwanshi N, Raju K. 2021. BPSO based feature selection for rice plant leaf disease detection with random forest classifier. Int J Eng Trends Technol, 69(4): 34-43. |
| [64] | Sowmyalakshmi R, Jayasankar T, Ayyem PiIllai V, Subramaniyan K, Pustokhina I V, Pustokhin D A, Shankar K. 2021. An optimal classification model for rice plant disease detection. CMC- Comput Mat Contin, 68(2): 1751-1767. |
| [65] | Tejaswini P, Singh P, Ramchandani M, Rathore Y K, Janghel R R. 2022. Rice leaf disease classification using CNN. IOP Conf Ser: Earth Environ Sci, 1032(1): 012017. |
| [66] | 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. J Integr Environ Sci, 13(1): 19-35. |
| [67] | Venu Vasantha S, Samreen S, Lakshmi Aparna Y. 2022. Rice disease diagnosis system (RDDS). CMC-Comput Mat Contin, 73(1): 1895-1914. |
| [68] | Wang C X, Caragea D, Kodadinne Narayana N, Hein N T, Bheemanahalli R, Somayanda I M, Krishna Jagadish S V. 2022. Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature. Plant Meth, 18(1): 9. |
| [69] | Winter J M, Young C A, Mehta V K, Ruane A C, Azarderakhsh M, Davitt A, McDonald K, Haden V R, Rosenzweig C. 2017. Integrating water supply constraints into irrigated agricultural simulations of California. Environ Model Softw, 96: 335-346. |
| [70] | Xia L, Zhao F, Chen J, Yu L, Lu M, Yu Q Y, Liang S F, Fan L L, Sun X, Wu S R, Wu W B, Yang P. 2022. A full resolution deep learning network for paddy rice mapping using Landsat data. ISPRS J Photogramm Remote Sens, 194: 91-107. |
| [71] | Xiao K H, Xiao D Q, Luo X W. 2010. Smart water-saving irrigation system in precision agriculture based on wireless sensor network. Trans Chin Soc Agric Eng, 26(11): 170-175. (in Chinese with English abstract) |
| [72] | Xie S N, Artachinda O N, Yang J, Liu H G. 2014. Rice policy reviews in China, Thailand and Vietnam: Policy instruments, targets and impacts. In: Proceedings of Selected Articles of 2013 World Agricultural Outlook Conference. Berlin, Heidelberg: Springer: 117-134. |
| [73] | Xu C, Jiang H Y, Yuen P, Zaki Ahmad K, Chen Y. 2020. MHW-PD: A robust rice panicles counting algorithm based on deep learning and multi-scale hybrid window. Comput Electron Agric, 173: 105375. |
| [74] | Zakzouk S, Ehab M, Atef S, Yousri R, Tawfik R M, Darweesh M S. 2022. A comparative study of machine learning techniques for automatic rice crop irrigation. In: 17th International Computer Engineering Conference (ICENCO). December 29-30, 2021. Cairo, Egypt: 46-50. |
| [75] | Zia H M, Fatima H S, Khurram M, Hassan I U, Ghazal M. 2022. Rapid testing system for rice quality control through comprehensive feature and kernel-type detection. Foods, 11(18): 2723. |
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