Cotton Genomics and Genetics 2025, Vol.16, No.2, 48-56 http://cropscipublisher.com/index.php/cgg 55 Hou X., Fan J., Zhang F., Hu W., and Xiang Y., 2024, Optimization of water and nitrogen management to improve seed cotton yield, water productivity and economic benefit of mulched drip-irrigated cotton in southern Xinjiang, China, Field Crops Research, 308: 109301. https://doi.org/10.1016/j.fcr.2024.109301 John D., Hussin N., Shahibi M., Ahmad M., Hashim H., and Ametefe D., 2023, A systematic review on the factors governing precision agriculture adoption among small-scale farmers, Outlook on Agriculture, 52(4): 469-485. https://doi.org/10.1177/00307270231205640 Jumanov D., Kuziboyev J., and Izzatullayev L., 2022, Agricultural technology and cotton yield, In: IOP conference series: earth and environmental science, IOP Publishing, 1112(1): 012025. https://doi.org/10.1088/1755-1315/1112/1/012025 Keswani B., Mohapatra A., Keswani P., Khanna A., Gupta D., and Rodrigues J., 2020, Improving weather dependent zone specific irrigation control scheme in IoT and big data enabled self driven precision agriculture mechanism, Enterprise Information Systems, 14(9-10): 1494-1515. https://doi.org/10.1080/17517575.2020.1713406 Kuang N., Hao C., Liu D., Maimaitiming M., Xiaokaitijiang K., Zhou Y., and Li Y., 2024, Modeling of cotton yield responses to different irrigation strategies in Southern Xinjiang Region, China, Agricultural Water Management, 303: 109018. https://doi.org/10.1016/j.agwat.2024.109018 Lambert D., Paudel K., and Larson J., 2015, Bundled adoption of precision agriculture technologies by cotton producers, Journal of Agricultural and Resource Economics, 40(2): 325-345. Lang P., Zhang L., Huang C., Chen J., Kang X., Zhang Z., and Tong Q., 2023, Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province, Frontiers in Plant Science, 13: 1048479. https://doi.org/10.3389/fpls.2022.1048479 Lowenberg‐DeBoer J., and Erickson B., 2019, Setting the record straight on precision agriculture adoption, Agronomy Journal, 111(4): 1552-1569. https://doi.org/10.2134/AGRONJ2018.12.0779 Mohyuddin G., Khan M., Haseeb A., Mahpara S., Waseem M., and Saleh A., 2024, Evaluation of machine learning approaches for precision farming in smart agriculture system: a comprehensive review, IEEE Access, 12: 60155-60184. https://doi.org/10.1109/ACCESS.2024.3390581 Naresh R., Singh N., Sachan P., Mohanty L., Sahoo S., Pandey S., and Singh B., 2024, Enhancing sustainable crop production through innovations in precision agriculture technologies, Journal of Scientific Research and Reports, 30(3): 89-113. https://doi.org/10.9734/jsrr/2024/v30i31861 Neely H., Morgan C., Stanislav S., Rouze G., Shi Y., Thomasson J., Valasek J., and Olsenholler J., 2016, Strategies for soil-based precision agriculture in cotton, In: Autonomous air and ground sensing systems for agricultural optimization and phenotyping, SPIE, 9866: 104-110. https://doi.org/10.1117/12.2228732 Nyéki A., and Neményi M., 2022, Crop yield prediction in precision agriculture, Agronomy, 12(10): 2460. https://doi.org/10.3390/agronomy12102460 Ofori M., and El-Gayar O., 2020, Drivers and challenges of precision agriculture: a social media perspective, Precision Agriculture, 22(3): 1019-1044. https://doi.org/10.1007/s11119-020-09760-0 Padhiary M., Saha D., Kumar R., Sethi L., and Kumar A., 2024, Enhancing precision agriculture: a comprehensive review of machine learning and AI vision applications in all-terrain vehicle for farm automation, Smart Agricultural Technology, 8: 100483. https://doi.org/10.1016/j.atech.2024.100483 Pal P., Landivar-Bowles J., Landivar-Scott J., Duffield N., Nowka K., Jung J., Chang A., Lee K., Zhao L., Pathak H., Brown P., and Best T., 2019, A systematic literature review of the factors affecting the precision agriculture adoption process, Precision Agriculture, 20(6): 1292-1316. https://doi.org/10.1007/s11119-019-09653-x Saha P., Kumar P., Kathuria S., Gehlot A., Pachouri V., and Duggal A., 2023, Precision agriculture using internet of things and wireless sensor networks, 2023 international conference on disruptive technologies (ICDT), IEEE, pp.519-522. https://doi.org/10.1109/ICDT57929.2023.10150678 Sanches G., Bordonal R., Magalhães P., Otto R., Chagas M., Cardoso T., and Luciano A., 2023, Towards greater sustainability of sugarcane production by precision agriculture to meet ethanol demands in south-central Brazil based on a life cycle assessment, Biosystems Engineering, 229: 57-68. https://doi.org/10.1016/j.biosystemseng.2023.03.013 Sanjeevi P., Prasanna S., Sivakumar B., Gunasekaran G., Alagiri I., and Anand R., 2020, Precision agriculture and farming using internet of things based on wireless sensor network, Transactions on Emerging Telecommunications Technologies, 31(12): e3978. https://doi.org/10.1002/ett.3978 Shafi U., Mumtaz R., García-Nieto J., Hassan S., Zaidi S., and Iqbal N., 2019, Precision agriculture techniques and practices: from considerations to applications, Sensors, 19(17): 3796. https://doi.org/10.3390/s19173796 Sharma A., Jain A., Gupta P., and Chowdary V., 2021, Machine learning applications for precision agriculture: a comprehensive review, IEEE Access, 9: 4843-4873. https://doi.org/10.1109/ACCESS.2020.3048415
RkJQdWJsaXNoZXIy MjQ4ODYzNA==