Sugarcane acreage estimation using satellite imagery and machine learning

Authors

  • MEGHARANI B. MAYANI Government Polytechnic Belgaum, Electronics and Communication Engineering, Department of Collegiate and Technical Education, Government of Karnataka, and Visvesvaraya Technological University, Belagavi 590018, India https://orcid.org/0000-0003-0121-5129
  • RAJESHWARI L. ITAGI KLE Institute of Technology, Electronics and Communication Department, Hubballi – Karnataka, and Visvesvaraya Technological University, Belagavi 590018, India

DOI:

https://doi.org/10.54386/jam.v26i4.2669

Keywords:

Cloud Computing, Crop Phenology, Small Holding, Machine Learning, Sugarcane

References

Aghababaei M, Ebrahimi A, Naghipour A, Asadi E and Verrelst J (2021). Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform. J. Rem Sens, 13: 4683. https://doi.org/10.3390/rs13224683

Altalak M, Ammaduddin M, Alajmi A and Rizg A (2022). Smart Agriculture Applications Using Deep Learning Technologies: A Survey, MDPI J. App. Sci., 12: 5919. https://doi.org/10.3390/app12125919

Arab S, Noguchi R, Matsushita S and Ahamed T (2021). Prediction of grape yields from time- series vegetation indices using satellite remote sensing and a machine-learning approach. J Rem. Sens. Apps., Soc. Env. 22: 100485, https://doi.org/10.1016/j.rsase.2021.100485

Everingham L, Lowe H, Donald A, Coomans H, Markley J. (2007), Advanced satellite imagery to classify sugarcane crop characteristics, Agron. Sustain. Dev., 27(2): 111–117. https://doi.org/10.1051/agro:2006034

Lonare A, Maheshwari B, Chinnasamy P (2022). Village level identification of sugarcane in Sangali, Maharashtra using open-source data, J. Agrometeorol., 24(3):249-254. https://doi.org/10.54386/jam.v24i3.1688

Luo C, Qi B, Huanjun Liu, Guo D, Lu L, Fu Q and Shao Y (2021). Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine, J. Rem Sens, 13:561. https://doi.org/10.3390/rs13040561

Malik K, Verma D, Srivastava S, Mehta S, Kumari N, Verma A, Singh P (2019). Sugarcane Production and Its Utilization as a Biofuel in India: Status, Perspectives, and Current Policy, Sugarcane Biofuels, 123-138. doi. org/10.1007/978-3-030-18597-8_6

Sreedhar R, Varshney A and Madhu D (2022). Sugarcane crop classification using time series analysis of optical and SAR sentinel images: a deep learning approach, Article Rem. Sens. Letters, 13(8): 812–821. http://dx.doi.org/10.1080/2150704X.2022.2088254

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Published

01-12-2024

How to Cite

MAYANI , M. B., & ITAGI , R. L. (2024). Sugarcane acreage estimation using satellite imagery and machine learning. Journal of Agrometeorology, 26(4), 505–508. https://doi.org/10.54386/jam.v26i4.2669

Issue

Vol. 26 No. 4 (2024): December

Section

Short Communication

Categories

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Copyright (c) 2024 MEGHARANI B. MAYANI , RAJESHWARI L. ITAGI

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