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CloudFactory Helps Hummingbird Technologies Farm for the Future
技术
- 分析与建模 - 数据即服务
- 分析与建模 - 边缘分析
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 农业
适用功能
- 现场服务
- 质量保证
用例
- 农场监控与精准农业
- 预测性维护
- 远程资产管理
- 远程协作
- 远程控制
服务
- 数据科学服务
- 系统集成
- 培训
挑战
Hummingbird Technologies faced the challenge of tagging and annotating vast amounts of data captured from drones and satellites to build accurate machine learning models for crop analytics. The process was highly domain-specific and time-consuming, requiring expertise in agronomy and remote sensing. The company needed a scalable solution to handle the increasing volume of data and to ensure the accuracy and reliability of their AI models, which are critical for providing actionable insights to farmers. Additionally, they had to continuously update their models to account for fluctuations in climate and other irregularities, which added another layer of complexity to their operations.
关于客户
Hummingbird Technologies is a company that provides crop analytics through machine learning algorithms applied to remote sensing imagery captured by drones and satellites. Founded by Will Wells, the company aims to help farmers increase their yields, optimize the use of inputs, and farm more sustainably. Hummingbird Technologies has developed 70 different machine learning-based products with over 90% accuracy, helping farmers improve their agrochemical efficiency by 20-30% on average. The company was incubated at Imperial College in London and has expanded its services to multiple countries, including the U.K., Brazil, Australia, Ukraine, Russia, Canada, and Malawi. Their mission is to deliver products that solve real-life problems for farmers, enabling them to save the environment without compromising yields or livelihoods.
解决方案
Hummingbird Technologies partnered with CloudFactory to handle the data annotation process, which is crucial for building accurate machine learning models. CloudFactory provided a dedicated team of annotators who worked closely with Hummingbird's data scientists and agronomists. This collaboration allowed Hummingbird to scale their data annotation efforts without overburdening their internal resources. The use of deep learning techniques and pre-annotated data significantly increased productivity and reduced the time required to build new models. Hummingbird also employed data augmentation techniques to make their models more robust, allowing them to adapt to various conditions such as changes in climate or crop protection methods. This approach ensured that their AI models remained accurate and reliable, providing actionable insights to farmers.
运营影响
数量效益
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