sugarcane Computer Vision Project
Updated a month ago
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downloadsHere are a few use cases for this project:
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Agriculture Management: Farmers and agricultural professionals can use the model to accurately identify and monitor the growth of sugarcane. Early detection of weed growth in sugarcane fields can help prevent crop damage and increase yield.
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Bioenergy Production: Bioenergy firms could apply the model to identify sugarcane abundance in certain areas for planning and optimizing biofuel production, as sugarcane is a primary source of biomass for bioenergy.
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Remote Sensing: Satellite or aerial image analysts could use the model to monitor large sugarcane plantations, assess health and detect potential infestations or diseases at an early stage.
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Agrochemical Companies: Companies producing pesticides or fertilizers could employ this model for field-testing of their products, by measuring efficiency against weed control in sugarcane fields.
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Agricultural Robotics: The model can be used in automation for precision farming. Agricultural robots equipped with this model could identify and remove weed in sugarcane fields, reducing the need for manual labor and potentially harmful herbicides.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
sugarcane-3vhxz_dataset,
title = { sugarcane Dataset },
type = { Open Source Dataset },
author = { Hoku },
howpublished = { \url{ https://universe.roboflow.com/hoku/sugarcane-3vhxz } },
url = { https://universe.roboflow.com/hoku/sugarcane-3vhxz },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { oct },
note = { visited on 2024-11-21 },
}