Page 33 - วิศวกรรมสาร ปีที่ 77 ฉบับที่ 2 เมษายน - มิถุนายน 2567
P. 33
การใช้ภาพถ่ายดาวเทียมในการประเมินพื้นที่เพาะปลูกอ้อย
Aworka, R., Cedric, L. S., Adoni, W. Y. H., Zoueu, J. T., Mutombo, F. K., Kimpolo, C. L. M., . . . Krichen, M. (2022).
Agricultural decision system based on advanced machine learning models for yield prediction: Case of East
African countries. Smart Agricultural Technology, 2, 100048. doi:https://doi.org/10.1016/j.atech.2022.100048
Dimov, D., Uhl, J. H., Löw, F., & Seboka, G. N. (2022). Sugarcane yield estimation through remote sensing
time series and phenology metrics. Smart Agricultural Technology, 2, 100046. doi:https://doi.org/10.1016/j.
atech.2022.100046
Fattori Junior, I. M., dos Santos Vianna, M., & Marin, F. R. (2022). Assimilating leaf area index data into a
sugarcane process-based crop model for improving yield estimation. European Journal of Agronomy, 136,
126501. doi:https://doi.org/10.1016/j.eja.2022.126501
Ghosh, Swarnendu & Dey, Subhadip & Bhogapurapu, Narayana & Homayouni, Saeid & Bhattacharya, Avik &
McNairn, Heather. (2022). Gaussian Process Regression Model for Crop Biophysical Parameter Retrieval
from Multi-Polarized C-Band SAR Data. Remote Sensing. 14. 934. 10.3390/rs14040934.
Gopikrishnan, S., Srivastava, G., & Priakanth, P. (2022). Improving sugarcane production in saline soils with
Machine Learning and the Internet of Things. Sustainable Computing: Informatics and Systems, 100743.
doi:https://doi.org/10.1016/j.suscom.2022.100743
Intarat, K. (2022). Land use classification in Nakhon Nayok Province using machine learning algorithms and
Sentinel-2 image. Burapha Science Journal (วารสาร วิทยาศาสตร์ บูรพา), 27(2), 1153-1171.
Kalaiarasi, E., & Anbarasi, A. (2022). Multi-parametric multiple kernel deep neural network for crop yield
prediction. Materials Today: Proceedings. doi:https://doi.org/10.1016/j.matpr.2022.03.115
Khavarian, Hassan & Aghaei, Maryam & Mostafazadeh, Raoof & Rabiei, Hamid. (2022). Assessment of machine
learning algorithms in land use classification. 10.1016/B978-0-323-89861-4.00022-1.
Luciano, A. C. d. S., Campagnuci, B. C. G., & le Maire, G. (2022). Mapping 33 years of sugarcane evolution in São Paulo
state, Brazil, using landsat imagery and generalized space-time classifiers. Remote Sensing Applications: Society
and Environment, 26, 100749. doi:https://doi.org/10.1016/j.rsase.2022.100749
Mishra, D., Pathak, G., Singh, B. P., Mohit, Sihag, P., Rajeev, Singh, K., & Singh, S. (2022). Crop classification by using
dual-pol SAR vegetation indices derived from Sentinel-1 SAR-C data. Environmental Monitoring and
Assessment, 195(1), 115. https://doi.org/10.1007/s10661-022-10591-x
Nihar, A., Patel, N. R., Pokhariyal, S., & Danodia, A. (2022). Sugarcane Crop Type Discrimination and Area Mapping
at Field Scale Using Sentinel Images and Machine Learning Methods. Journal of the Indian Society of Remote
Sensing, 50(2), 217-225. doi:10.1007/s12524-021-01444-0
Som-Ard, J., Atzberger, C., Izquierdo-Verdiguier, E., Vuolo, F., & Immitzer, M. (2021). Remote sensing applications
in sugarcane cultivation: A review. Remote Sensing, 13(20). doi:10.3390/rs13204040
Tripathi, Akshar & Tiwari, Reet & Tiwari, Surya Prakash. (2022). A deep learning multi-layer perceptron and
remote sensing approach for soil health based crop yield estimation. International Journal of Applied
Earth Observation and Geoinformation. 113. 102959. 10.1016/j.jag.2022.102959.
วิศวกรรมสาร ปีที่ 77 ฉบับที่ 2 เมษายน - มิถุนายน 2567 33

