Case Studies > CloudFactory Helps Hummingbird Technologies Farm for the Future

CloudFactory Helps Hummingbird Technologies Farm for the Future

Company Size

11-200

Region

Africa
America
Asia
Europe

Country

Australia
Brazil
Canada
Russia
Ukraine
United Kingdom

Product

CloudFactory
Hummingbird Technologies

Tech Stack

Machine Learning
Remote Sensing
Data Augmentation
Deep Learning

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Customer Satisfaction
Environmental Impact Reduction
Innovation Output
Productivity Improvements

Technology Category

Analytics & Modeling - Data-as-a-Service
Analytics & Modeling - Edge Analytics
Analytics & Modeling - Machine Learning
Analytics & Modeling - Predictive Analytics

Applicable Industries

Agriculture

Applicable Functions

Field Services
Quality Assurance

Use Cases

Farm Monitoring & Precision Farming
Predictive Maintenance
Remote Asset Management
Remote Collaboration
Remote Control

Services

Data Science Services
System Integration
Training

About The Customer

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.

The Challenge

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.

The Solution

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.

Operational Impact

Hummingbird Technologies was able to scale their data annotation efforts significantly, allowing them to handle larger datasets and develop more accurate machine learning models.
The collaboration with CloudFactory enabled Hummingbird to free up internal resources, allowing their data scientists to focus on more complex tasks rather than spending time on data labeling.
The use of data augmentation techniques made Hummingbird's models more robust, enabling them to adapt to various conditions and maintain high accuracy levels.
The partnership with CloudFactory facilitated continuous communication and feedback loops, ensuring that any errors in the dataset were quickly identified and corrected.
Hummingbird's AI models provided reliable information to farmers, helping them make informed decisions that improved crop yields and reduced the use of agrochemicals.

Quantitative Benefit

Hummingbird's customers typically see an increase in agrochemical efficiency by 20-30% on average.
The accuracy of Hummingbird's machine learning models is over 90%.
The time required to upgrade models to account for new conditions, such as changes in climate, was reduced to just a couple of days.