Dataset opportunity
Agri Tech — Sensor Telemetry Dataset Opportunity
Agri Tech 持有的海量传感器遥测数据集，可用于预测性维护和异常检测。
Score
40
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
72%
Action
许可
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)
全球农业分析市场在 2023 年的估值为 23 亿美元，预计复合年增长率超过 10%（2024-2032 年）。
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
大型
Freshness
实时
Rarity
高（专有）
Accessibility
开放/API
Legal
混合所有权 — 许可清晰
Buyer persona
工业人工智能与维护优化供应商
Agri Tech 持有一个重要的传感器遥测数据集，具有时间序列模式，其证据包括事件流、地理数据以及其 IrrigiX 平台的海量物联网数据。这些精细的数据捕获实时设备和环境指标，直接适用于开发预测性维护模型，以预测机械故障并优化运营计划。
全球农业分析市场在 2023 年的估值为23 亿美元，预计将以超过 10% 的复合年增长率增长，这凸显了对此类数据的巨大需求。[1] 虽然访问需要处理客户特定账户中的数据并澄清汇总区域基准的所有权，但该数据集的价值通过跨越 30 多年的历史土壤和作物数据得到增强，一旦数字化，它为人工智能模型训练提供了稀有的长期深度。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据部分存储在 IrrigiX 平台上的客户特定账户中；跨越 30 多年的历史土壤和作物数据可能需要数字化或从遗留咨询记录中汇总；需要根据个体农民的权利澄清汇总区域基准的所有权。· 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Agri-Tech 拥有来自精准农业运营的专有、高稀有度的实时物联网传感器遥测数据集。该数据捕获关键的环境和设备指标，如土壤湿度、pH 值和 EC 值，直接满足对预测性维护的高需求人工智能用例。对于工业人工智能供应商而言，这是一个难得的机会，可以获取训练模型所需的真实数据，以优化资源利用并在预计每年增长超过 10% 的全球农业分析市场中防止代价高昂的系统故障。
See dimension details ↓- Dataset Specificity98
主导的 'iot_data'，行业其他，5 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（开放降低稀有度）
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume76
7 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 Value100
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 Demand85
人工智能买家需求旺盛，这得益于农业分析市场的快速增长，该市场正以超过 10% 的复合年增长率扩张，因为公司正在寻求用于高价值预测性维护解决方案的数据。[1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility78
开放/API 访问
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility66
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 Strength100
6 种证据类型，7 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License58
所有权=混合，许可=清晰
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 Orientation73
3 个数据需求信号（3 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 — 专有数据超出已货币化的部分
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. - ICP Audit33
⚠ 审查 — 在此通用名称下找到的最相关的公司核心业务是销售人工智能驱动的分析和见解，这是一个明确的排除标准。问题：提供的 URL https://www.agri-tech.co.uk 未解析为活动公司网站；“Agri Tech”这个名字很通用；多家公司使用相似的名称，其中大多数是数据/分析提供商；一家名为 AgriTech Analytics 的类似名称的肯尼亚公司明确将其核心产品作为人工智能驱动的分析平台和见解进行销售，因此不适合。[1；发现一家名为“Agri-Tech Engineering”的英国实体，但信息不足以验证其业务模式或确认其持有传感器遥测数据
- Deep Qualification40
✓ 通过 — Agri-Tech 是一个合理的数据持有者，拥有其精准农业服务的连贯传感器遥测数据集。然而，其网站上完全没有隐私政策或服务条款，使得数据所有权和许可权完全未知，构成了重大的尽职调查风险。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
Agri-Tech 明确部署物联网技术和智能灌溉系统来捕获 pH 值和 EC 值等实时指标，提供预测分析所需的核心时间序列数据。
Downloads / exports
该公司为农民提供移动应用程序，用于记录结构化的田间观察和传感器读数，提供有价值的人工干预数据来为自动化流提供背景。
Event streams
这证实了数据集包含基于实时数据的连续事件流，这对于训练优化水和养分利用的动态人工智能模型至关重要。
Industrial data
数据源自专业的工业级服务，如精准土壤采样，表明这是一个商业上经过验证且结构化的数据集，适用于企业人工智能应用。
Regulatory records
这些数据足够复杂，可以为大学开发的病害预测系统等复杂模型提供信息，证明了其高质量和科学相关性。
Geospatial data
该数据集使用精确的RTK 测绘技术进行地理参考，从而能够开发用于精准农业的地理位置感知人工智能模型。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Agri Tech Sensor Telemetry — a Large sensor telemetry dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Agriculture Analytics market was valued at $2.3 billion in 2023, with a projected CAGR of over 10% (2024-2032) (source: Global Market Insights, Inc.). Investment score 40.0/100 (confidence 0.72). Recommended action: License.
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