Leveraging precision agricultural tools for enhanced crop protection in rice cultivation
DOI:
https://doi.org/10.14719/pst.8199Keywords:
Artificial Intelligence (AI), biosensors, rice, remote sensing, robots, Unmanned Aerial Vehicles (UAVs)Abstract
Rice production is crucial for meeting the food demand of the ever-growing global population. However, it is impeded by biotic stressors viz., insect pests, diseases and weeds. Traditionally, farmers and plant protection experts rely on conventional crop protection measures to combat these challenges. These measures have various drawbacks including intensive labour requirement, higher cultivation costs, untimely pest detection and indiscriminate agrochemical use which adversely affects the consumers and the environment. Precision tools and technologies can address all these issues to benefit the farmers and agricultural ecosystem on a sustainable basis. Remote sensing technologies aid in weed mapping and detecting disease and pest incidence in rice fields by evaluating the changes in crop reflectance brought about by biotic stressors. Agricultural robotic systems are multifunctional and have attained more than 80 % correct image classification, 96 % weed control and less than 10 % crop damage. Unmanned aerial vehicles for pesticide spraying are cost-effective substitutes for manual spray and can reduce spray volume by more than 20 times besides good application efficiency and effective control. Artificial intelligence offers precise solutions for biotic stress identification and control. Biosensors have also been developed for aiding in detecting rice blast, false smut, tungro incidence and bacterial leaf blight. Apart from highlighting the utilization of precision agriculture tools and technologies for plant protection and weed control in rice, this article also reviews the challenges and prospects related to its application and its feasibility for the stakeholders utilising them to gain sustainable rice production.
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FAOSTAT. Food and Agriculture Organization Corporate Statistical Database (FAOSTAT); Food and Agriculture Organization of the United Nations Database; Food and Agriculture Organization (FAO), Rome. Available online: http://www.fao.org (accessed on 27 May 2024). 2021.
Pandey Sushil, Byerlee D, Dawe D, Dobermann A, Mohanty S, Scott R, et al. Strategic Research and Policy Issues for Food Security. Rice in the Global Economy. 2010.
Iqbal J, Zia-ul-Qamar, Yousaf U, Asgher A, Dilshad R, Qamar FM, et al. Sustainable rice production under biotic and abiotic stress challenges. In: Sustainable Agriculture in the Era of the OMICs Revolution. Springer International Publishing; 2023. p. 241–68. https://doi.org/10.1007/978-3-031-15568-0_11
Ke Y, Deng H, Wang S. Advances in understanding broad?spectrum resistance to pathogens in rice. The Plant Journal. 2017;90(4):738-48. https://doi.org/10.1111/tpj.13438
Pathak MD, Khan ZR, International Rice Research Institute., International Centre of Insect Physiology and Ecology. Insect pests of rice. International Rice Research Institute; 1994. 89 p.
Mondal D, Ghosh A, Roy D, Kumar A, Shamurailatpam D, Bera S, et al. Yield loss assessment of rice (Oryza Sativa L.) due to different biotic stresses under system of rice intensification (SRI). J Entomol Zool Stud. 1974;5(4):1974–80.
Chauhan BS, Johnson DE. Relative importance of shoot and root competition in dry-seeded rice growing with junglerice (Echinochloa colona) and ludwigia (Ludwigia hyssopifolia). Weed science. 2010;58(3):295-9. https://doi.org/10.1614/ws-d-09-00068.1
Mandal N, Das DK, Sahoo RN, Adak S, Kumar A, Viswanathan C, Mukherjee J, Rajashekara H, Ranjan R, Das B. Assessing rice blast disease severity through hyperspectral remote sensing. Journal of Agrometeorology. 2022;24(3):241-8. https://doi.org/10.1614/ws-d-09-00068.1
Latif G, Alghazo J, Maheswar R, Vijayakumar V, Butt M. Deep learning based intelligence cognitive vision drone for automatic plant diseases identification and spraying. Journal of Intelligent & Fuzzy Systems. 2020;39(6):8103-14. https://doi.org/10.3233/JIFS-189132
Zhao D, Cao Y, Li J, Cao Q, Li J, Guo F, Feng S, Xu T. Early detection of rice leaf blast disease using unmanned aerial vehicle remote sensing: A novel approach integrating a new spectral vegetation index and machine learning. Agronomy. 2024;14(3):602. https://doi.org/10.3390/agronomy14030602
Partel V, Charan Kakarla S, Ampatzidis Y. Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Comput Electron Agric. 201;157:339–50. https://doi.org/10.1016/j.compag.2018.12.048
The 2010 European Union Report on Pesticide Residues in Food. EFSA Journal. 2013;11(3). https://doi.org/10.2903/j.efsa.2013.3130
Jeanmart S, Edmunds AJ, Lamberth C, Pouliot M. Synthetic approaches to the 2010–2014 new agrochemicals. Bioorganic & Medicinal Chemistry. 2016;24(3):317-41. https://doi.org/10.1016/j.bmc.2015.12.014
Meena RS, Kumar S, Datta R, Lal R, Vijayakumar V, Brtnicky M, et al. Impact of agrochemicals on soil microbiota and management: A review. Land. 2020;9(2):34. https://doi.org/10.3390/land9020034
Ulimwengu JM, Kibonge A. Climate-Smart Agriculture Practices Based on Precision Agriculture: The Case of Maize in Western Congo. In: A thriving agricultural sector in a changing climate: Meeting Malabo Declaration goals through climate-smart agriculture. 2016.
Tripathi R, Kumar A, Guru P, Debnath M, Mohapatra S, Mohanty S, et al. Precision farming technologies for water and nutrient management in rice: Challenges and opportunities. Oryza-An International Journal on Rice. 2021;58(Special):126–42. https://doi.org/10.35709/ory.2021.58.spl.5
Groher T, Heitkämper K, Walter A, Liebisch F, Umstätter C. Status quo of adoption of precision agriculture enabling technologies in Swiss plant production. Precision Agriculture. 2020;21:1327-50. https://doi.org/10.1007/s11119-020-09723-5
Gharde Y, Singh PK, Dubey RP, Gupta PK. Assessment of yield and economic losses in agriculture due to weeds in India. Crop Protection. 2018;107. https://doi.org/10.1016/j.cropro.2018.01.007
Andres A, Freitas GD, Concenço G, Melo PT, Ferreira FA. Performance of rice cultivar BRS pelota and control of (Echinochloa spp.) submitted to four flooding times after reduced herbicide dose application. Planta Daninha. 2007;25:859-67. https://doi.org/10.1590/s0100-83582007000400023
Johnson DE, Wopereis MC, Mbodj D, Diallo S, Powers S, Haefele SM. Timing of weed management and yield losses due to weeds in irrigated rice in the Sahel. Field Crops Research. 2004;85(1):31-42. https://doi.org/10.1016/S0378-4290(03)00124-2
Thorp KR, Tian LF. A review on remote sensing of weeds in agriculture. Precision Agriculture. 2004;5(5):477-508. https://doi.org/10.1007/s11119-004-5321-1
Bajwa AA, Mahajan G, Chauhan BS. Nonconventional Weed Management Strategies for Modern Agriculture. Weed Sci. 2015;63(4):723–47. https://doi.org/10.1614/ws-d-15-00064.1
Maes WH, Steppe K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science. 2019;24(2):152-64. https://doi.org/10.1016/j.tplants.2018.11.007
Gašparovi? M, Zrinjski M, Barkovi? ?, Rado?aj D. An automatic method for weed mapping in oat fields based on UAV imagery. Comput Electron Agric. 2020;173. https://doi.org/10.1016/j.compag.2020.105385
Vasileiou M, Kyrgiakos LS, Kleisiari C, Kleftodimos G, Vlontzos G, Belhouchette H, et al. Transforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning. Crop Protection. 2024;176. https://doi.org/10.1016/j.cropro.2023.106522
Hakme E, Herrmann SS, Poulsen ME. Data processing approach for the screening and quantification of pesticide residues in food matrices for early-generation GC-TOFMS. Brazilian Journal of Analytical Chemistry. 2020;7(26):51-77. https://doi.org/10.30744/BRJAC.2179-3425.AR-36-2019
Wang P, Tang Y, Luo F, Wang L, Li C, Niu Q, et al. Weed25: A deep learning dataset for weed identification. Frontiers in Plant Science. 2022;13:1053329. https://doi.org/10.3389/fpls.2022.1053329
Al-Doski J, Mansor1 SB, Zulhaidi H, Shafri M. Image classification in remote sensing. 2013;3(10). www.iiste.org
Alirezazadeh P, Schirrmann M, Stolzenburg F. A comparative analysis of deep learning methods for weed classification of high-resolution UAV images. Journal of Plant Diseases and Protection. 2024;131(1):227-36. https://doi.org/10.1007/s41348-023-00814-9
Ma X, Deng X, Qi L, Jiang Y, Li H, Wang Y, et al. Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields. PloS one. 2019;14(4):e0215676. https://doi.org/10.1371/journal.pone.0215676
Ashraf T, Khan YN. Weed density classification in rice crop using computer vision. Comput Electron Agric. 2020;175. https://doi.org/10.1016/j.compag.2020.105590
Kamath R, Balachandra M, Prabhu S. Paddy Crop and Weed Discrimination: A multiple classifier system approach. International Journal of Agronomy. 2020;2020. https://doi.org/10.1155/2020/6474536
Dadashzadeh M, Abbaspour-Gilandeh Y, Mesri-Gundoshmian T, Sabzi S, Hernández-Hernández JL, Hernández-Hernández M, et al. Weed classification for site-specific weed management using an automated stereo computer-vision machine-learning system in rice fields. Plants. 2020;9(5):559. https://doi.org/10.3390/plants9050559
Peng H, Li Z, Zhou Z, Shao Y. Weed detection in paddy field using an improved RetinaNet network. Computers and Electronics in Agriculture. 2022;199:107179. https://doi.org/10.1016/j.compag.2022.107179
Yuan C, Liu T, Gao F, Zhang R, Seng X. YOLOv5s-CBAM-DMLHead: A lightweight identification algorithm for weedy rice (Oryza sativa f. spontanea) based on improved YOLOv5. Crop Protection. 2023;172. https://doi.org/10.1016/j.cropro.2023.106342
Young SL, Meyer GE, Woldt WE. Future directions for automated weed management in precision agriculture. InAutomation: The future of weed control in cropping systems 2013 Oct 18 (pp. 249-259). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-007-7512-1_15
Osten V. A review of technologies that can be enabled by robotics to improve weed control in Australian Cotton Farming Systems A commissioned report by SwarmFarm Robotics, Gindie Queensland. 2016.
Young SL. True integrated weed management. Weed Res. 2012;52(2). https://doi.org/10.1111/j.1365-3180.2012.00903.x
Wu X, Aravecchia S, Lottes P, Stachniss C, Pradalier C. Robotic weed control using automated weed and crop classification. Journal of Field Robotics. 2020;37(2):322-40. https://doi.org/10.1002/rob.21938
Gerhards R, Andujar Sanchez D, Hamouz P, Peteinatos GG, Christensen S, Fernandez?Quintanilla C. Advances in site?specific weed management in agriculture—A review. Weed Research. 2022;62(2):123-33. https://doi.org/10.1111/wre.12526
Rasmussen J, Azim S, Nielsen J. Pre-harvest weed mapping of Cirsium arvense L. based on free satellite imagery–The importance of weed aggregation and image resolution. European Journal of Agronomy. 2021;130:126373. https://doi.org/10.1016/j.eja.2021.126373
Choi KH, Han SK, Han SH, Park KH, Kim KS, Kim S. Morphology-based guidance line extraction for an autonomous weeding robot in paddy fields. Comput Electron Agric. 2015;113. https://doi.org/10.1016/j.compag.2015.02.014
Zhang Q, Chen MS, Li B. A visual navigation algorithm for paddy field weeding robot based on image understanding. Computers and Electronics in Agriculture. 2017;143:66-78. https://doi.org/10.1016/j.compag.2017.09.008
Kanagasingham S, Ekpanyapong M, Chaihan R. Integrating machine vision-based row guidance with GPS and compass-based routing to achieve autonomous navigation for a rice field weeding robot. Precision Agriculture. 2020;21(4):831-55. https://doi.org/10.1007/s11119-019-09697-z
Nakai S, Yamada Y. Development of a weed suppression robot for rice cultivation: weed suppression and posture control. Int J Electr Comput Electron Commun Eng. 2014;8:1736-40.
Sori H, Inoue H, Hatta H, Ando Y. Effect for a paddy weeding robot in wet rice culture. Journal of Robotics and Mechatronics. 2018;30(2):198-205. https://doi.org/10.20965/jrm.2018.p0198
Development of a remote control type weeding machine with stirring chains for a paddy field. In CLAWAR Association Limited; 2019.
Koulousis A, Kalaitzidis D, Bechtsis D, Yfoulis C, Tsolakis N, Bochtis D. A weed control unmanned ground vehicle prototype for precision farming activities: The case of red rice. In: Springer Optimization and Its Applications. 2022. https://doi.org/10.1007/978-3-030-84152-2_7
Ju J, Chen G, Lv Z, Zhao M, Sun L, Wang Z, Wang J. Design and experiment of an adaptive cruise weeding robot for paddy fields based on improved YOLOv5. Computers and Electronics in Agriculture. 2024;219:108824. https://doi.org/10.1016/j.compag.2024.108824
ARVAtec Srl. The Paddy Robot MOONDINO. https://www.arvatec.it/en/prodotti/arvatec/moondino/.
Naik BS, Shashikala J, Krishnamurthy YL. Study on the diversity of endophytic communities from rice (Oryza sativa L.) and their antagonistic activities in vitro. Microbiol Res. 2009;164(3):290-96. https://doi.org/10.1016/j.micres.2006.12.003
Nayak S, Samanta S, Sengupta C, Swain SS. Rice crop loss due to major pathogens and the potential of endophytic microbes for their control and management. J Appl Biol Biotechnol. 2021;9(5):166-75. https://doi.org/10.7324/JABB.2021.9523
Yang B, Zhu W, Rezaei EE, Li J, Sun Z, Zhang J. The optimal phenological phase of maize for yield prediction with high-frequency uav remote sensing. Remote Sens (Basel). 2022;14(7):1559. https://doi.org/10.3390/rs14071559
Zheng Q, Huang W, Cui X, Shi Y, Liu L. New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery. Sensors (Switzerland). 2018;18(3):868. https://doi.org/10.3390/s18030868
Kobayashi T, Sasahara M, Kanda E, Ishiguro K, Hase S, Torigoe Y. Assessment of rice panicle blast disease using airborne hyperspectral imagery. Open Agric J. 2016;10(1). https://doi.org/10.2174/1874331501610010028
Ma B, Cao G, Hu C, Chen C. Monitoring the rice panicle blast control period based on uav multispectral remote sensing and machine learning. Land (Basel). 2023;12(2):469. https://doi.org/10.3390/land12020469
Zhang D, Zhou X, Zhang J, Lan Y, Xu C, Liang D. Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PLoS One. 2018;13(5):e0187470. https://doi.org/10.1371/journal.pone.0187470
Hongo C, Isono S, Sigit G, Tamura E. Efficient damage assessment of rice bacterial leaf blight disease in agricultural insurance using UAV data. Agronomy. 2024;14(6):1328. https://doi.org/10.3390/agronomy14061328
Mirandilla JRF, Yamashita M, Yoshimura M, Paringit EC. Leaf spectral analysis for detection and differentiation of three major rice diseases in the Philippines. Remote Sens (Basel). 2023;15(12):3058. https://doi.org/10.3390/rs15123058
Zhang J, Zhou X, Shen D, Yu Q, Yuan L, Dong Y. Development of spectral features for monitoring rice bacterial leaf blight disease using broad-band remote sensing systems. Phyton-International Journal of Experimental Botany. 2024;93(4):745–62. https://doi.org/10.32604/phyton.2024.049734
Wang Y, Xing M, Zhang H, He B, Zhang Y. Rice false smut monitoring based on band selection of UAV hyperspectral data. Remote Sens (Basel). 2023;15(12):2961. https://doi.org/10.3390/rs15122961
Lin F, Li B, Zhou R, Chen H, Zhang J. Early detection of rice sheath blight using hyperspectral remote sensing. remote sens (Basel). 2024;16(12):2047. https://doi.org/10.3390/rs16122047
Kundu M, Krishnan P, Kotnala RK, Sumana G. Recent developments in biosensors to combat agricultural challenges and their future prospects. Trends in food science & technology. 2019;88:157-78. https://doi.org/10.1016/j.tifs.2019.03.024
Faizal Azizi MM, Lau HY. Advanced diagnostic approaches developed for the global menace of rice diseases: a review. Canadian Journal of Plant Pathology. 2022;44(5):627-51. https://doi.org/10.1080/07060661.2022.2053588
Fang Y, Ramasamy RP. Current and prospective methods for plant disease detection. Biosensors. 2015;5(3):537-61. https://doi.org/10.3390/bios5030537
Ahmed FK, Alghuthaymi MA, Abd-Elsalam KA, Ravichandran M, Kalia A. Nano-Based Robotic Technologies for Plant Disease Diagnosis. In: Nanorobotics and Nanodiagnostics in Integrative Biology and Biomedicine. 2022. https://doi.org/10.1007/978-3-031-16084-4_14
Yang W, Zhang H, Li M, Wang Z, Zhou J, Wang S, et al. Early diagnosis of blast fungus, Magnaporthe oryzae, in rice plant by using an ultra-sensitive electrically magnetic-controllable electrochemical biosensor. Anal Chim Acta. 2014;850. https://doi.org/10.1016/j.aca.2014.08.040
Rana K, Mittal J, Narang J, Mishra A, Pudake RN. Graphene based electrochemical DNA biosensor for detection of false smut of rice (Ustilaginoidea virens). Plant Pathol J (Faisalabad). 2021;37(3):291. https://doi.org/10.5423/PPJ.OA.11.2020.0207
Uda MN, Hasfalina CM, Samsuzanaa AA, Faridah S, Gopinath SC, Parmin NA, et al. A disposable biosensor based on antibody-antigen interaction for tungro disease detection. InNanobiosensors for Biomolecular Targeting. 2019 Jan 1 (pp. 147-164). Elsevier. https://doi.org/10.1016/B978-0-12-813900-4.00006-3
Awaludin N, Abdullah J, Salam F, Ramachandran K, Yusof NA, Wasoh H. Fluorescence-based immunoassay for the detection of Xanthomonas oryzae pv. oryzae in rice leaf. Anal Biochem. 2020;610. https://doi.org/10.1016/j.ab.2020.113876
Razali H, Awaludin N, Husin NH, Zulkepli SA, Abd Rahman R, Ismail MR, et al. Development of an electrochemical immunosensor strip for early detection of rice bacterial leaf blight (blb) disease and its application on a portable device. Malaysian Journal of Analytical Sciences. 2022;26(6):1191-204.
Bashir K, Rehman M, Bari M. Detection and classification of rice diseases: An automated approach using textural features. Mehran University Research Journal of Engineering & Technology. 2019;38(1):239-50. https://doi.org/10.22581/muet1982.1901.20
Prajapati HB, Shah JP, Dabhi VK. Detection and classification of rice plant diseases. Intelligent Decision Technologies. 2017;11(3):357-73. https://doi.org/10.3233/IDT-170301
Pushpa Athisaya Sakila Rani A, Suresh Singh N. Protecting the environment from pollution through early detection of infections on crops using the deep belief network in paddy. Total Environment Research Themes. 2022;3–4. https://doi.org/10.1016/j.totert.2022.100020
Hajjar MJ, Ahmed N, Alhudaib KA, Ullah H. Integrated insect pest management techniques for rice. Sustainability. 2023;15(5):4499. https://doi.org/10.3390/su15054499
Kobayashi T, Kanda E, Kitada K, Ishiguro K, Torigoe Y. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology. 2001;91(3):316-23. https://doi.org/10.1094/PHYTO.2001.91.3.316
Xu T, Wang F, Yi Q, Xie L, Yao X. A bibliometric and visualized analysis of research progress and trends in rice remote sensing over the past 42 years (1980–2021). Remote Sensing. 2022;14(15):3607. https://doi.org/10.3390/rs14153607
Prasannakumar NR, Chander S, Sahoo RN, Gupta VK. Assessment of brown planthopper, (Nilaparvata lugens)[Stål], damage in rice using hyperspectral remote sensing. International Journal of Pest Management. 2013;59(3):180-88. https://doi.org/10.1080/09670874.2013.808780
Fan Y, Wang T, Qiu Z, Peng J, Zhang C, He Y. Fast detection of striped stem-borer (Chilo suppressalis Walker) infested rice seedling based on visible/near-infrared hyperspectral imaging system. Sensors. 2017;17(11):2470. https://doi.org/10.3390/s17112470
Wan Q, Ouyang A, Liu Y, Xiong Z, Li X, Li L. Detection of infestation by striped stem?borer (Chilo suppressalis) in rice based on hyperspectral imaging. Journal of Food Process Engineering. 2022;45(11):e14142. https://doi.org/10.1111/jfpe.14142
Morsy M. Monitoring and managing rice pest infestation through hyperspectral remote sensing technology under field conditions. Journal of Applied Plant Protection. 2020;9(1):67-82. https://doi.org/10.21608/japp.2020.178429
Wang J, Yu K, Zhu X, Zhu F, Tian M, Wang Z. Monitoring of rice leaf folder damage based on remote sensing methods. In: 2018 7th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2018. 2018. https://doi.org/10.1109/Agro-Geoinformatics.2018.8476138
Chen C, Bao Y, Zhu F, Yang R. Remote sensing monitoring of rice growth under Cnaphalocrocis medinalis (Guenée) damage by integrating satellite and UAV remote sensing data. International Journal of Remote Sensing. 2024;45(3):772-90. https://doi.org/10.1080/01431161.2024.2302350
Guan S, Takahashi K, Watanabe S, Tanaka K. Unmanned aerial vehicle-based techniques for monitoring and prevention of invasive apple snails (Pomacea canaliculata) in rice paddy fields. Agriculture. 2024;14(2):299. https://doi.org/10.3390/agriculture14020299
Kariyanna B, Sowjanya M. Unravelling the use of artificial intelligence in management of insect pests. Smart Agricultural Technology. 2024;29:100517. https://doi.org/10.1016/j.atech.2024.100517
Yao Q, Lv J, Liu Q jie, Diao G qiang, Yang B jun, Chen H ming, et al. An insect imaging system to automate rice light-trap pest identification. J Integr Agric. 2012;11(6). https://doi.org/10.1016/S2095-3119(12)60089-6
Muppala C, Guruviah V. Detection of leaf folder and yellow stemborer moths in the paddy field using deep neural network with search and rescue optimization. Information processing in Agriculture. 2021;8(2):350-8. https://doi.org/10.1016/j.inpa.2020.09.002
Sun G, Liu S, Luo H, Feng Z, Yang B, Luo J, et al. Intelligent monitoring system of migratory pests based on searchlight trap and machine vision. Frontiers in Plant Science. 2022;13:897739. https://doi.org/10.3389/fpls.2022.897739
Hassan SI, Alam MM, Illahi U, Suud MM. A new deep learning-based technique for rice pest detection using remote sensing. Peer J Computer Science. 2023;9:e1167. https://doi.org/10.7717/PEERJ-CS.1167
Hu Y, Deng X, Lan Y, Chen X, Long Y, Liu C. Detection of rice pests based on self-attention mechanism and multi-scale feature fusion. Insects. 2023;14(3):280. https://doi.org/10.3390/insects14030280
Anastasiou E, Fountas S, Koutsiaras M, Voulgaraki M, Vatsanidou A, Barreiro-Hurle J, et al. Precision farming technologies on crop protection: A stakeholders survey. Smart Agricultural Technology. 2023;5:100293. https://doi.org/10.1016/j.atech.2023.100293
Vecchio Y, De Rosa M, Adinolfi F, Bartoli L, Masi M. Adoption of precision farming tools: A context-related analysis. Land use policy. 2020;94:104481. https://doi.org/10.1016/j.landusepol.2020.104481
Aubert BA, Schroeder A, Grimaudo J. IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology. Decision Support Systems. 2012;54(1):510-20. https://doi.org/10.1016/j.dss.2012.07.002
Nowak B. Precision agriculture: Where do we stand? A review of the adoption of precision agriculture technologies on field crops farms in developed countries. Agricultural Research. 2021;10(4):515-22. https://doi.org/10.1007/s40003-021-00539-x
Masi M, De Rosa M, Vecchio Y, Bartoli L, Adinolfi F. The long way to innovation adoption: insights from precision agriculture. Agricultural and Food Economics. 2022;10(1):27. https://doi.org/10.1186/s40100-022-00236-5
Hundal GS, Laux CM, Buckmaster D, Sutton MJ, Langemeier M. Exploring barriers to the adoption of internet of things-based precision agriculture practices. Agriculture. 2023;13(1):163. https://doi.org/10.3390/agriculture13010163
Li S, Feng Z, Yang B, Li H, Liao F, Gao Y, et al. An intelligent monitoring system of diseases and pests on rice canopy. Frontiers in Plant Science. 2022;13:972286. https://doi.org/10.3389/fpls.2022.972286
Stroppiana D, Villa P, Sona G, Ronchetti G, Candiani G, Pepe M, et al. Early season weed mapping in rice crops using multi-spectral UAV data. Int J Remote Sens. 2018;39(15–16). https://doi.org/10.1080/01431161.2018.1441569
Huang H, Lan Y, Deng J, Yang A, Deng X, Zhang L, et al. A semantic labeling approach for accurate weed mapping of high resolution UAV imagery. Sensors (Switzerland). 2018;18(7). https://doi.org/10.3390/s18072113
Huang H, Deng J, Lan Y, Yang A, Deng X, Wen S, et al. Accurate weed mapping and prescription map generation based on fully convolutional networks using UAV imagery. Sensors (Switzerland). 2018;18(10). https://doi.org/10.3390/s18103299
Huang H, Lan Y, Yang A, Zhang Y, Wen S, Deng J. Deep learning versus Object-based Image Analysis (OBIA) in weed mapping of UAV imagery. Int J Remote Sens. 2020;41(9). https://doi.org/10.1080/01431161.2019.1706112
Lan Y, Huang K, Yang C, Lei L, Ye J, Zhang J, et al. Real-time identification of rice weeds by UAV low-altitude remote sensing based on improved semantic segmentation model. Remote Sensing. 2021;13(21):4370. https://doi.org/10.3390/rs13214370
Kawamura K, Asai H, Yasuda T, Soisouvanh P, Phongchanmixay S. Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm. Plant Production Science. 2021;24(2):198-215. https://doi.org/10.1080/1343943X.2020.1829490
Yu F, Jin Z, Guo S, Guo Z, Zhang H, Xu T, et al. Research on weed identification method in rice fields based on UAV remote sensing. Frontiers in Plant Science. 2022;13:1037760. https://doi.org/10.3389/fpls.2022.1037760
Guo Z, Cai D, Zhou Y, Xu T, Yu F. Identifying rice field weeds from unmanned aerial vehicle remote sensing imagery using deep learning. Plant Methods. 2024;20(1):105. https://doi.org/10.1186/s13007-024-01232-0
Kumar Singh A, Raja Bs. Classification of rice disease using digital image processing and SVM classifier. International Journal of Electrical and Electronics Engineers ISSN. 2015;07(01).
Feng S, Zhao D, Guan Q, Li J, Liu Z, Jin Z, et al. A deep convolutional neural network-based wavelength selection method for spectral characteristics of rice blast disease. Comput Electron Agric. 2022;199. https://doi.org/10.1016/j.compag.2022.107199
Liu ZY, Wu HF, Huang JF. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Computers and Electronics in Agriculture. 2010;72(2):99-106. https://doi.org/10.1016/j.compag.2010.03.003
Narmadha RP, Sengottaiyan N, Kavitha RJ. Deep transfer learning based rice plant disease detection model. Intelligent Automation & Soft Computing. 2022;31(2). https://doi.org/10.32604/iasc.2022.020679
Gautam V, Trivedi NK, Singh A, Mohamed HG, Noya ID, Kaur P, et al. A transfer learning-based artificial intelligence model for leaf disease assessment. Sustainability. 2022;14(20):13610. https://doi.org/10.3390/su142013610
Kumar VS, Jaganathan M, Viswanathan A, Umamaheswari M, Vignesh JJ. Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model. Environmental Research Communications. 2023;5(6):065014. https://doi.org/10.1088/2515-7620/acdece
Abasi AK, Makhadmeh SN, Alomari OA, Tubishat M, Mohammed HJ. Enhancing rice leaf disease classification: a customized convolutional neural network approach. Sustainability. 2023;15(20):15039. https://doi.org/10.3390/su152015039
Nagarajan VR. Precision farming with AI: An integrated deep learning solution for paddy leaf disease monitoring. International Journal of Advanced Computer Science & Applications. 2024;15(7). https://doi.org/10.14569/IJACSA.2024.0150783
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Copyright (c) 2025 Satyadeep Sonowal, G Prabukumar, M Senthivelu, M Gnanachithra, V Babu Rajendra Prasad , P Murali Arthanari, V Manivannan , Pranab Deka, Prithibiraj Bhuyan
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