rice_kernel_detection Computer Vision Project
Updated 2 years ago
Here are a few use cases for this project:
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Quality Control in Rice Production: The model can be integrated into a quality control system at a rice processing plant to automatically detect the levels of broken kernels and whole grains, thereby enhancing the efficiency and accuracy of the sorting process.
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Automated Harvesting: The model could be implemented in agricultural machines to detect the state of rice kernels during harvesting, enabling the automation of harvesting only high-quality, unbroken kernels.
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Food Safety Inspections: Regulatory authorities could use the model to routinely inspect rice supplies for broken or contaminated kernels, ensuring the safety and quality of rice distributed in the market.
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Agricultural Research: Researchers could utilize the model in studies that investigate the factors contributing to rice kernel breakage, aiming to enhance crop yield and quality.
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Rice Grading Applications: The model could be used in grading applications where the quality of rice is determined based on the percentage of broken kernels, helping establish a transparent and standardized rice trade and pricing system.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
rice_kernel_detection_dataset,
title = { rice_kernel_detection Dataset },
type = { Open Source Dataset },
author = { ricekernel },
howpublished = { \url{ https://universe.roboflow.com/ricekernel/rice_kernel_detection } },
url = { https://universe.roboflow.com/ricekernel/rice_kernel_detection },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { apr },
note = { visited on 2024-12-31 },
}