Bioscience Evidence 2024, Vol.14, No.6, 260-269 http://bioscipublisher.com/index.php/be 268 Herrero-Huerta M., Rodriguez-Gonzalvez P., and Rainey K., 2020, Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean, Plant Methods, 16: 78. https://doi.org/10.1186/s13007-020-00620-6 Jemo M., Devkota K., Epule T., Chfadi T., Moutiq R., Hafidi M., Silatsa F., and Jibrin J., 2023, Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria, Frontiers in Plant Science, 14: 1120826. https://doi.org/10.3389/fpls.2023.1120826 Loures L., Chamizo A., Ferreira P., Loures A., Castanho R., and Panagopoulos T., 2020, Assessing the effectiveness of precision agriculture management systems in mediterranean small farms, Sustainability, 12(9): 3765. https://doi.org/10.3390/su12093765 Maimaitijiang M., Sagan V., Sidike P., Hartling S., Esposito F., and Fritschi F., 2020, Soybean yield prediction from UAV using multimodal data fusion and deep learning, Remote Sensing of Environment, 237: 111599. https://doi.org/10.1016/j.rse.2019.111599 Monzon J., Calviño P., Sadras V., Zubiaurre J., and Andrade F., 2018, Precision agriculture based on crop physiological principles improves whole-farm yield and profit: a case study, European Journal of Agronomy, 99: 62-71. https://doi.org/10.1016/J.EJA.2018.06.011 Ravelombola W., Qin J., Shi A., Song Q., Yuan J., Wang F., Chen P., Yan L., Feng Y., Zhao T., Meng Y., Guan K., Yang C., and Zhang M., 2021, Genome-wide association study and genomic selection for yield and related traits in soybean, PLoS ONE, 16(8): e0255761. https://doi.org/10.1371/journal.pone.0255761 Ren P., Li H., Han S., Chen R., Yang G., Yang H., Feng H., and Zhao C., 2023, Estimation of soybean yield by combining maturity group information and unmanned aerial vehicle multi-sensor data using machine learning, Remote Sensing, 15(17): 4286. https://doi.org/10.3390/rs15174286 Ruan J., Wang Y., Chan F., Hu X., Zhao M., Zhu F., Shi B., Shi Y., and Lin F., 2019, A life cycle framework of green iot-based agriculture and its finance, operation, and management issues, IEEE Communications Magazine, 57: 90-96. https://doi.org/10.1109/MCOM.2019.1800332 Sachin K., Dass A., Dhar S., Rajanna G., Singh T., Sudhishri S., Sannagoudar M., Choudhary A., Kushwaha H., Praveen B., Prasad S., Sharma V., Pooniya V., Krishnan P., Khanna M., Singh R., Varatharajan T., Kumari K., Nithinkumar K., San A., and Devi A., 2023a, Sensor-based precision nutrient and irrigation management enhances the physiological performance, water productivity, and yield of soybean under system of crop intensification, Frontiers in Plant Science, 14: 1282217. https://doi.org/10.3389/fpls.2023.1282217 Sachin K., Dass A., Dhar S., Rajanna G., Singh T., Sudhishri S., Kushwaha H., and Khanna M., 2023b, Precision nutrient and irrigation management influences the growth, rhizosphere characters and yield of soybean (Glycine max) under system of crop intensification, The Indian Journal of Agricultural Sciences, 93(8): 856-861. https://doi.org/10.56093/ijas.v93i8.136822 Shook J., Gangopadhyay T., Wu L., Ganapathysubramanian B., Sarkar S., and Singh A., 2020, Crop yield prediction integrating genotype and weather variables using deep learning, PLoS ONE, 16(6): e0252402. https://doi.org/10.1371/journal.pone.0252402 Shu K., 2020, Prediction of soybean yield using self-normalizing neural networks, Proceedings of the 2020 5th International Conference on Machine Learning Technologies, pp.51-55. https://doi.org/10.1145/3409073.3409092 Skakun S., Kalecinski N., Brown M., Johnson D., Vermote E., Roger J., and Franch B., 2021, Assessing within-field corn and soybean yield variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 satellite imagery, Remote. Sens., 13: 872. https://doi.org/10.3390/rs13050872 Smidt E., Conley S., Zhu J., and Arriaga F., 2016, Identifying field attributes that predict soybean yield using random forest analysis, Agronomy Journal, 108: 637-646. https://doi.org/10.2134/AGRONJ2015.0222 Teodoro P., Teodoro L., Baio F., Junior C., Santos R., Ramos A., Pinheiro M., Osco L., Gonçalves W., Carneiro A., Junior J., Pistori H., and Shiratsuchi L., 2021, Predicting days to maturity, plant height, and grain yield in soybean: a machine and deep learning approach using multispectral data, Remote. Sens., 13: 4632. https://doi.org/10.3390/rs13224632 Vogel J., Liu W., Olhoft P., Crafts-Brandner S., Pennycooke J., and Christiansen N., 2021, Soybean yield formation physiology – a foundation for precision breeding based improvement, Frontiers in Plant Science, 12: 719706. https://doi.org/10.3389/fpls.2021.719706 Wang Y., 2024, GWAS reveals progress in genes related to rice yield and quality, Rice Genomics and Genetics, 15(2): 48-57. https://doi.org/10.5376/rgg.2024.15.0006
RkJQdWJsaXNoZXIy MjQ4ODYzMg==