Download PDF
Orchard Robotics Leverages Scale Rapid for Precision Crop Management
Technology Category
- Analytics & Modeling - Computer Vision Software
- Analytics & Modeling - Machine Learning
Applicable Industries
- Agriculture
- Education
Applicable Functions
- Quality Assurance
Use Cases
- Autonomous Robots
- Visual Quality Detection
Services
- Data Science Services
- Testing & Certification
The Challenge
Orchard Robotics, a company providing AI-first precision crop management solutions to farmers, faced a significant challenge in collecting and utilizing precision data across vast commercial orchards. The company developed tractor-mounted, AI-powered camera systems to collect precision data about every tree. However, the company needed to accurately count every fruit on every tree, a task that proved to be incredibly difficult and tedious, especially when the fruit was small. As a small team, Orchard Robotics struggled to scale these annotations in-house. They initially tried using three other major data-labeling services, but they could not achieve the consistent quality they needed. The quality varied dramatically between batches, and they could not provide feedback to the annotators on the quality of the labels. These platforms also did not offer ellipses as an annotation type, forcing Orchard Robotics to rely on bounding boxes, a less-than-ideal option when labeling spherical fruit.
About The Customer
Orchard Robotics is a company that provides farmers with an AI-first approach to precision crop management. They have developed tractor-mounted, AI-powered camera systems that collect precision data about every tree in an orchard. Their Orchard OS software platform integrates with existing farm operations, allowing farmers to act on this data directly. By using machine learning models to extract insights from terabytes of image data, Orchard Robotics enables farmers to manage their crops precisely, producing more food for the world, much more efficiently.
The Solution
Orchard Robotics turned to Scale Rapid for high-quality annotations with attention to detail. Scale Rapid provided results for their annotation batches in as little as 12 hours, a significant reduction from the 4-5 days it took with previous services. The quality of their annotations also improved, with Scale Rapid returning high-quality annotations, even for complex, high-resolution images. Scale Rapid allowed Orchard Robotics to provide direct feedback to annotators and monitor annotation progress, ensuring reliable, high-quality annotations within a clearly defined timeline. Scale Rapid also automatically controlled the quality of the labels and provided an API that allowed Orchard Robotics to automate their annotation requests as they continued to grow.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Intelligent Farming with ThingWorx Analytics
Z Farms was facing three challenges: costly irrigation systems with water as a limited resource, narrow optimal ranges of soil moisture for growth with difficult maintenance and farm operators could not simply turn on irrigation systems like a faucet.
Case Study
Greenhouse Intelligent Monitoring and Control Solution
Farming Orchids is the most successful form of precision farming in Taiwan, and also the most exported flower. Orchids need a specific temperature and humidity conditions to grow and bloom, and its flowering time may not be in line with market demands, so the price collapses when there is overproduction. Therefore, some farmers began to import automated greenhouse control systems for breeding and forcing, which not only improves quality, but also effectively controls the production period and yield to ensure revenue. In 2012, an orchid farmer built a Forcing Greenhouse of about 200 pings (approximately 661 Square Meters) in Tainan, Taiwan. The system integrator adopted Advantech’s APAX-5000 series programmable automation controllers to build the control platform, coupled with Advantech WebAccess HMI/SCADA software, to achieve cloud monitoring. The staff of the orchid field can monitor important data anytime via smart phone, iPad, and other handheld devices, and control the growth and flowering conditions. System requirements: In the past, most environmental control systems of orchid greenhouses in Taiwan used PLCs (Programmable Logic Controller) with poorscalability and control, and could not be connected to the Internet formonitoring from the cloud. For advanced database analysis and networking capability, the PC platform must be adopted. Therefore, PAC Systems (Programmable Automation Controller) with both PLC programming capabilities andPC functions is a better choice.The environmental control of the Orchid greenhouse switches on and off devices like fan, shade net, cooling/heat pump, liquid flow control, water-cooling wall etc. It is controlled by a control panel of electric controllers, and is driven by a motor, to adjust the greenhouse temperature, humidity, and other environmental conditions to the set parameters.
Case Study
Enabling Internet of Things Innovation in Agriculture
DigiBale, wanted to apply technology know-how and IP from implementations successfully to more agriculture sectors including cotton, forestry, sugarcane and cattle. However, farmers and growers still have worries about the connected technology.
Case Study
Precision beekeeping with wireless temperature monitoring
Honeybees are insects of large economic value and provide a vital service to agriculture by pollinating a variety of crops. In addition, bees provide us with valuable products such as honey, beeswax, propolis, bee venom, etc. Monitoring of honeybee colony health, population, productivity, and environmental conditions affecting the colony health have always been exceedingly difficult tasks in apiculture. Research has shown that even small deviations (by more than 2°C) from the optimal temperatures have a significant influence on the development of the brood and the health of adult bees.
