Transforming Fruit and Vegetable Harvesting & Analytics with Computer Vision

Ulrik Stig Hansen
August 22, 2023
5 min read
blog image

Automated harvesting and analytics company Four Growers uses Encord’s platform and annotators to help build its training datasets from scratch labeling millions of instances of greenhouses and plants. So far, Encord has halved the time it takes Four Growers to get its datasets in working order. 

Customer: Meet Four Growers

Four Growers was founded to provide healthy, affordable, local produce by reducing the production costs of greenhouse growers through autonomous harvesting robots. 

The GR-100 robot has a robotic arm, calibrated by four stereo cameras that allow it to precisely detect and harvest produce using computer vision, focusing on tomatoes initially. Now in its second iteration, the GR-100 also offers heat mapping and yield forecasting abilities, as well as patented packing technology. 

By automating the harvesting process, Four Growers hopes to minimize food waste, increase quality, and contribute to making produce more affordable for everyone. 

Problem: Insufficient Access to Annotated Datasets Hinders Development of Automated Harvesting Models

Currently, commercial greenhouses are expansive and it is impossible for manual pickers to get all of the ripe fruits on time. Over time, this leads to a large quantity of fruit just rotting in the greenhouse. 

Hoping to expedite this process, Four Growers set about developing their harvesting and analytics robot. But when it came to training its deep learning models for automated harvesting, Four Growers found that while there is a lot of awareness around growing in greenhouses, there was a distinct lack of publicly available data sets to draw from. 

This meant that the company had to build its own datasets from scratch, initially employing a number of freelance contractors to manually annotate all of the data, which consisted mostly of images of greenhouses and tomato plants. 

This process was incredibly time-consuming and manual. By having multiple external annotators using various tools, the Four Growers team had to often deal with multiple different annotation platforms at once, consolidating the data into one succinct database at a later stage. 

For Mira Murali, Computer Vision Engineer at Four Growers, this manual approach was eating into her schedule every week taking as long as 10 hours to manually review and condense data from multiple annotators that came through in various formats.

However, despite the huge amount of time, it was taking to have the data manually labeled and then verified by the team at Four Growers, it felt like a necessary task given the importance of having clean accurate data to begin with.  

“You can tweak your models all you want but if your data at the beginning is wrong, then no matter how good your model is, you won’t get the end resource that you want,” Mira says.

Solution: Encord Annotate for automated labeling and a team of annotators

The team at Four Growers needed a data annotation tool that could handle the labeling of millions of object instances at the same time and this is where Encord came into the mix. 

Initially, the team was attracted to Encord because not only did the platform offer automatic annotating, but it also allowed Four Growers to work with an in-house team of human annotators. So rather than having different freelance contractors using different systems that had to be later consolidated, this streamlined approach instantly gave the Four Growers team time back in their days to focus on innovating their product and growing the business. 

“The fact that Encord was able to offer both an automatic labeling tool as well as a team of annotators that we could use was very attractive. We need that flexibility at this stage in our journey,” says Murali.  

Encord Active is another tool beginning to be utilized by the Four Growers team. It is an open-source toolkit that can be used for testing, validating, and evaluating your models. This allows Four Growers to further segment their datasets and build on their predictive model, making the harvesting even more precise.

Results: Significant Time Reduction and Streamlined Annotation Process Achieved with Encord's Platform

For Four Growers, a fast-growing and lean business, the biggest win with using Encord has been the time saved. Now, instead of spending hours upon hours dealing with contractors and re-verifying all of that work, the team can simply upload their images to Encord’s platform, wait for a ping to say the annotation work is complete, and focus on their core jobs. 

Speaking about the experience with Encord’s platform, Murali says: 

“Using Encord has more than halved the time it takes us to provide feedback on imagery. What could have previously taken us 2 hours now takes approximately 30 minutes.” 

Four Growers currently works with a number of large commercial greenhouses located in the US, Canada, and the Netherlands. As the company continues to grow its business and expand into more regions and crops, the company plans to continue its journey with Encord, leaning into the data analysis aspect of the platform to further understand how the team can improve its model, further streamline harvesting and help reduce food waste.

author-avatar-url
Written by Ulrik Stig Hansen
Ulrik is the President & Co-Founder of Encord. Ulrik started his career in the Emerging Markets team at J.P. Morgan. Ulrik holds an M.S. in Computer Science from Imperial College London. In his spare time, Ulrik enjoys writing ultra-low latency software applications in C++ and enjoys exper... see more
View more posts

Think Encord could be a good fit for your team as well?

Book a demo

Software To Help You Turn Your Data Into AI

Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.