Precision agriculture means harnessing technology to optimize production. It relies on precise granular data at the individual plant level, on the scale of large industrial farms, supporting everything from weed identification to crop health and yield estimation. This understanding can then inform real-time actions, for example the application of herbicide to an individual weed. This is the challenge that Fafaza addresses: deploying AI ‘at the edge’, on the back of a moving tractor and without the need for connectivity.

Although AI techniques have been able to achieve plant recognition for a number of years, the challenge has been in moving from powerful specialist platforms with delayed processing of data, to processing and acting in real time: this is ‘AI at the edge’. To be technically practical, a system must be fast enough to distinguish and identify plants using ambient light and to apply treatment while the plant is still in view. To be commercially viable, a system must be rugged and affordable.

Fafaza has been developed to run on off-the-shelf components, including a low-cost camera that can capture images at around 20 frames per second and an AI compute platform that costs less than $100. Major processor vendors continue to invest heavily in devices that can run AI inference algorithms, bringing costs down further. These developments are opening up new areas for real-time AI processing in the field, without the need to rely on a communications infrastructure or the cloud.

AI at the edge, using relatively simple, low-cost and low-power devices to perform complex AI tasks without needing to send data to the cloud, will unleash a new wave of applications, from autonomous vehicles and smart cities through to predictive maintenance on industrial machinery.

Niall Mottram, Head of Industrial and Energy at Cambridge Consultants, commented: “I’m really excited by the innovations that moving AI processing to the edge will bring in precision agriculture. Connectivity is generally poor in rural environments and the ability to run complex machine learning based applications on a tractor implement opens up many new opportunities to collect, process and act on data in real time. Fafaza demonstrates how AI at the edge approaches can become practical, cost effective and deployable today.”

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