Dataset opportunity
Metos — 传感器遥测数据集机会
Metos 持有的中等传感器遥测数据集，可用于预测性维护和异常检测。
Score
70
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
收购
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
全球农场机械预测性维护市场 = 2024 年为 14.2 亿美元，复合年增长率为 17.8%（来源：农场机械预测性维护市场研究报告 2033）
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
Profile
Dataset profile
Type
传感器遥测数据集
Modality
时间序列
Sector
其他
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — 许可权待澄清
Buyer persona
工业人工智能与维护优化供应商
Metos 持有丰富的传感器遥测数据集，结构为时间序列数据，从农场硬件聚合而来。此集合包括iot_data、geo_data 和图像集合，提供了一个多模态基础，非常适合训练预测性维护模型，以预测设备故障、优化灌溉或预测作物病害。
全球农场机械预测性维护市场（2024 年达到14.2 亿美元，预计将以17.8% 的复合年增长率增长）凸显了其商业价值。[8] 虽然此数据的二次使用需要与拥有生成硬件的农民进行仔细的合同审查，但其聚合的、真实的性质使其成为任何旨在抓住这个快速扩张的市场份额的 AI 买家的极其有价值的资产。[8] ⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据由农民拥有的硬件生成，但在 FieldClimate 平台上聚合；公司同时销售硬件和数据驱动的智能（疾病模型）；农业数据的二次使用需要与最终用户进行仔细的合同审查 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据证明 Metos 拥有专有的、跨越数十年的农业传感器遥测集合，并辅以病虫害图像和深入的历史环境数据。对于任何开发农场机械预测性维护解决方案的 AI 供应商来说，这是一个关键资产，该市场价值 14.2 亿美元，年增长率接近 18%。该数据集独特地结合了时间序列物联网数据、图像和长期的地理空间记录，提供了训练强大 AI 所需的地面真实数据，这些 AI 能够预测设备故障并在各种真实条件下优化性能。
See dimension details ↓- Dataset Specificity74
主导的 'iot_data'，行业其他，3 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式传输
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适用于预测性维护
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
AI 买家需求旺盛，这得益于农业预测性维护市场的快速增长，预计该市场将以 17.8% 的复合年增长率扩张。[8]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 种证据类型，3 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License36
所有权=混合，许可=权利不明确
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation56
2 个数据胃口信号（2 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，5 个近期外部信号 — 超出已货币化的专有数据
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Live from Ag in Motion at the John Deere booth, Kristjan Hebert and Evan Shout are joined by Deanna Kovar, president of Deere's Ag and Turf Division. Kovar grew up on a Wisconsin dairy farm, started as a summer intern decades ago, and has since worked her way through nearly every corner of the company.... <a href="https://www.realagriculture.com/2026/07/the-truth-about-trust-and-tech-with-deanna-kovar-truth-about-ag-podcast-ep-63/">Read More</a></p>”
- “Terres Univia, Terres Inovia et la Fop estiment la production nationale de colza autour de 4,7 millions de tonnes en 2026, soit un niveau proche de 2025. Ce résultat est imputable à la hausse des surfaces, le rendement moyen étant en nette baisse sur un an.”
- “<p><img alt="" class="attachment-thumbnail size-thumbnail wp-post-image" height="150" src="https://d3hid44mqnfbhw.cloudfront.net/precisagms/wp-content/uploads/2026/07/SmartIrrigation-150x150.jpg" width="150" />New survey findings reveal how shifting priorities around water, costs, and resilience are reshaping investment decisions across modern agricultural operations.</p> <p>The post <a href="https://www.globalagtechinitiative.com/in-field-technologies/irrigation/what-growers-are-telling-the-global-irrigation-industry/">What Growers Are Telling the Global Irrigation Industry</a> appeared first”
IoT / sensor data
Metos 提供来自数千个现场传感器的广泛时间序列遥测数据，捕获土壤湿度和湿度等环境变量，这些变量对于模拟设备压力和预测组件故障至关重要。
Image collection
该集合包括来自自动陷阱的高分辨率病虫害图像，为 AI 模型提供了一个独特的上下文层，用于将环境事件与机械上的特定操作压力相关联。
Geospatial data
该数据集包含超过 35 年的地理空间环境数据，提供了跨越不同气候和作物类型的深入历史背景，这是构建和验证高度准确、可泛化的预测模型所必需的。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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