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水稻数据集 Commeo 和 Osmancik

发布时间：2026-03-17 14:31:57资源ID：2031263140241903618资源类型：免费

该数据集《Rice Dataset Commeo and Osmancik》主要用于多分类任务，数据形态以图像为主，应用场景偏向农业场景。 题目说明：Rice Dataset: 2 Class Commeo and Osmancik Rice 任务类型：图像多分类。 建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：未检测到标准 CSV，可优先查看目录中的索引或说明文件。 DATASET: https://www.muratkoklu.com/datasets/ 1: KOKLU, M., CINAR, I. and TASPINAR, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. DOI: https://doi.org/10.1016/j.compag.2021.106285 2: CINAR, I. and KOKLU, M. (2021). Determination of Effective and Specific Physical Features of Rice Varieties by Computer Vision In Exterior Quality Inspection. Selcuk Journal of Agriculture and Food Sciences, 35(3), 229 - 243. DOI: https://doi.org/10.15316/SJAFS.2021.252 3: CINAR, I. and KOKLU, M. (2022). Identification of Rice Varieties Using Machine Learning Algorithms. Journal of Agricultural Sciences, 28 (2), 307 - 325. DOI: https://doi.org/10.15832/ankutbd.862482 4: CINAR, I. and KOKLU, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, 7(3), 188 - 194. DOI: https://doi.org/10.18201/ijisae.2019355381

水稻数据集 Commeo 和 Osmancik

摘要概览

该数据集《Rice Dataset Commeo and Osmancik》主要用于多分类任务，数据形态以图像为主，应用场景偏向农业场景。 题目说明：Rice Dataset: 2 Class Commeo and Osmancik Rice

任务类型：图像多分类。

建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。

评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。

可用文件：未检测到标准 CSV，可优先查看目录中的索引或说明文件。

DATASET: https://www.muratkoklu.com/datasets/

1: KOKLU, M., CINAR, I. and TASPINAR, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. DOI: https://doi.org/10.1016/j.compag.2021.106285

2: CINAR, I. and KOKLU, M. (2021). Determination of Effective and Specific Physical Features of Rice Varieties by Computer Vision In Exterior Quality Inspection. Selcuk Journal of Agriculture and Food Sciences, 35(3), 229 - 243. DOI: https://doi.org/10.15316/SJAFS.2021.252

3: CINAR, I. and KOKLU, M. (2022). Identification of Rice Varieties Using Machine Learning Algorithms. Journal of Agricultural Sciences, 28 (2), 307 - 325. DOI: https://doi.org/10.15832/ankutbd.862482

4: CINAR, I. and KOKLU, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, 7(3), 188 - 194. DOI: https://doi.org/10.18201/ijisae.2019355381