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AI “Maths Robot” Helps Manage Microclimates and Increase Berry Yield Predictions

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One of the biggest agriculture/horticulture companies in Australia is Costa Group, and the company has recently employed an AI system intended to improve crop quality and yield by helping the company analyze its berry crops. As reported by ZDNet, the system that Costa Group employs was designed by The Yield, an AgTech company based in Sydney. The AI system analyzes 14 different features in order to derive meaningful insights. These features include temperature, soil conditions, wind, light, and rain. The information is then combined with an existing dataset and predictions about individual crops are returned.

Costa Group operates several berry farms located throughout Queensland, New South Wales, and Tasmania. The berry farms in these locations contain polytunnels, and these polytunnels have their own microclimates. Because the climate of these tunnels is controlled, they require their own “weather service”. Internet of Things (IoT) devices within the tunnels collect a wide variety of data that is fed into the AI model.  The process is one of continual model creation, production, feedback, and refinement. The creators of the system describe it as a “maths robot”.

Similar AI models have been used to predict crop yield for spinach, lettuce, and other crops, yet the founder of The Yield, Ros Harvey, explained that their system is critical because berries are challenging to monitor as they grow. Unlike other vegetables or fruits, berries often go through a variety of stages very quickly and a single berry crop can have many growth stages at the same time. As Harvey explained to ZDNet:

“It’s been such a difficult problem for berry producers globally because unlike other crops, berries have many growth stages all at the same time… If you look at a berry plant, it’s fruiting, flowering, there are berries that are ready, and there are berries that are half produced because it continually fruits when it’s in season. Whereas other crops go through this linear growth stage where you harvest once at the end of the season.”

Currently, AI is typically used for just a few different applications in the AgTech industry. Among these applications are precision farming, agriculture robots, livestock monitoring, and drone analytics. In 2018, precision farming accounted for around 35.6% of AI usage in the agricultural sector. Applications like the type developed by The Yield, which assist farming operations in increasing yield and shielding themselves from risk by gaining valuable insight into growing trends, seem poised to see much more use in the near future.

The data returned by the AI system allows for the Costa Group to gain a better understanding of the yield, which in turn helps the company manage its logistical costs and price point. Harvey predicts that in the future more and more companies will begin using AI-powered applications to quantify yield and reduce risk, noting that as climate change makes weather more unpredictable more companies may choose to use polytunnels as well. The use of AI across the entire agricultural industry is predicted to grow rapidly in the near future. Machine learning, computer vision, and predictive analytics are helping agricultural operations increase yield and do more with less.

As a recent report released on the state of AI in agriculture found, AI AgTech is expected to grow dramatically over the course of the next five years. In 2018, the AI market in agriculture was valued at around 330 million USD, yet it is expected to reach a value of approximately 980 million USD by the end of 2024. Other recent applications of AI in the agriculture sector include small robots designed to weed fields and keeping track of growing conditions in vertical farming operations.