Evaluation of computer vision pipeline for farm-level analytics: A case study in Sugarcane-Reference-Cited by-同舟云学术

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Evaluation of computer vision pipeline for farm-level analytics: A case study in Sugarcane

Published:2024-07-08 Issue: Volume:15 Page:238-247
ISSN:
Container-title:Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies
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Author:

Shikhar Sambal¹^ORCID,Ranjan Rajiv¹^ORCID,Sa Aman¹^ORCID,Srivastava Anshika¹^ORCID,Srivastava Yash¹^ORCID,Kumar Dinesh¹^ORCID,Tamaskar Shashank¹^ORCID,Sobti Anupam¹^ORCID

Affiliation:

1. Plaksha University, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3674829.3675079

Reference37 articles.

1. [n. d.]. Maxar Imagery. https://maxar.com/maxar-intelligence/products/optical-imagery. [Accessed 06-03-2024].

2. [n. d.]. Overview | Maps Static API | Google for Developers — developers.google.com. https://developers.google.com/maps/documentation/maps-static/overview. [Accessed 06-03-2024].

3. Estimation of sugarcane evapotranspiration from remote sensing and limited meteorological variables using machine learning models

4. Han Lin Aung, Burak Uzkent, Marshall Burke, David Lobell, and Stefano Ermon. 2020. Farm parcel delineation using spatio-temporal convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 76–77.

5. FAUNet: Frequency Attention U-Net for Parcel Boundary Delineation in Satellite Images;Awad Bahaa;Remote Sensing,2023

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