Using AI to predict berry yield
Australian agtech company The Yield Technology Solutions has announced its plans to expand its customer relationship with Costa Group, an Australia grower, packer and marketer of fresh fruit and vegetables. From February 2020, Costa will launch The Yield’s Sensing+ solution across eight berry farms in NSW, Queensland and Tasmania. The technology combines sensors and analytics to provide information and predictions in apps, helping commercial growers make on-farm decisions like when to irrigate, feed, plant, protect and harvest.
“We aim to better understand and manage the specific growing conditions that improve the quantity and quality of our yields. Our berries are grown in tunnels and Sensing+ measures the growing conditions in our microclimates and uses AI to give us in-tunnel weather predictions. We are also rolling out the Sensing+ Yield Predictions module, which predicts our berry yields using AI and data from both our harvest management systems and the microclimate weather. We have been impressed with the accuracy achieved to date compared with our current manual approach,” said Harry Debney, CEO of Costa Group.
The Sensing+ solution was developed by the Food Agility Cooperative Research Centre in collaboration with The Yield, Costa Group and the University of Technology Sydney (UTS), and is being commercialised six months into a two-year research program. The Yield’s platform was used to create artificial intelligence (AI) models that could predict the size and time of Costa’s berry yield, with UTS researchers planning to continually improve the models to be released into the commercial product in future.
“Using Costa’s extensive datasets, our platform enables us to quickly and efficiently combine data to create AI models for things like yield predictions that will drive significant commercial benefit for Costa Group,” said Ros Harvey, founder and Managing Director of The Yield.
Based in Sydney, The Yield uses Internet of Things (IoT), data science and AI to power its technology and solve challenges at farm level and throughout the food chain.
“This is a great example of how, using data and agile research methods, we can get research results out of the lab and into the field faster,” said Professor David Lamb, Food Agility Chief Scientist.
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