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Oh no! My dataset’s too small! OK so you have been brilliant at planning and have acquired a dataset of 20,000 images of Blackgrass. You proudly train the AI and it works in the field. Some of the time. You move to another field and the classification drops to around 10%. Oh dear. What’s gone…

Its so obvious to a skilled Agronomist as to what is soil, crop, crop debris, weeds stones… but to AI, not so much. A skilled Agronomist (I used to be one!) can walk into a field and immediately identify the crop and the weed species. Having said that, there are some published research where 12%…

A raw image means nothing to AI software. Using an unsupervised approach the AI can eventually learn to classify different objects based on certain features but that interpretation will mean nothing to a human. At some point the human needs to tell the AI what the crop looks like so that the AI, when it…

Whilst we can focus on field robots as an obvious Use Case for AI solutions trained on synthetic datasets, this is not the only market. It is a large market though extending into all parts of agriculture. Reverse phenotyping We can take real images and ‘reverse’ them into synthetic plants. This means that researchers can study…