地球资源数据云——数据资源详情
温室植物生长
该数据集《Greenhouse Plant Growth》主要用于多分类任务，数据形态以图像为主，应用场景偏向医疗健康。 题目说明：Advanced IoT - Based Smart Greenhouse Dataset (Agriculture Growth Monitoring) 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：Greenhouse Plant Growth Metrics.csv。 The Advanced IoT Agriculture Dataset captures detailed physiological and morphological measurements of plants grown under two greenhouse settings (IoT‑enabled vs. traditional) at Tikrit University’s Agriculture Lab. Compiled by Mohammed Ismail Lifta (2023–2024) under the supervision of Prof. Wisam Dawood Abdullah, it comprises 30,000 records spanning 14 variables that quantify chlorophyll levels, growth rates, biomass (wet/dry weight), root metrics, and more, alongside a final categorical Class label. Description 1. Data Collection Location & Period: Agriculture Lab, College of Computer Science & Mathematics, Tikrit University, Iraq (2023–2024).
摘要概览
该数据集《Greenhouse Plant Growth》主要用于多分类任务，数据形态以图像为主，应用场景偏向医疗健康。 题目说明：Advanced IoT - Based Smart Greenhouse Dataset (Agriculture Growth Monitoring)
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：Greenhouse Plant Growth Metrics.csv。
The Advanced IoT Agriculture Dataset captures detailed physiological and morphological measurements of plants grown under two greenhouse settings (IoT‑enabled vs. traditional) at Tikrit University’s Agriculture Lab. Compiled by Mohammed Ismail Lifta (2023–2024) under the supervision of Prof.
Wisam Dawood Abdullah, it comprises 30,000 records spanning 14 variables that quantify chlorophyll levels, growth rates, biomass (wet/dry weight), root metrics, and more, alongside a final categorical Class label.
Description
1. Data Collection
Location & Period: Agriculture Lab, College of Computer Science & Mathematics, Tikrit University, Iraq (2023–2024).