Apart from the primary control system we are working on the cultivating implement, which has an Ai camera attached that scans the grids of crops as in the video below:

This video shows how we've combined traditional, CPU intensive, Open CV type computer vision systems with modern YOLO26n Ai image inference to try and recognise, and predict, the position of grids of seedlings. The idea is to try and predict when there is a gap between the crops to momentarily stop the cultivator implement in the Y axis and quickly send a set of hydraulically driven rotating claws across the X axis, and back again. The red horizontal X axis line acts as a trigger point and when the system detects that a weighted combination of the inferred Ai driven grid center and the traditional CV grid center overlap, or are close enough to one another, a data point is added to the data array. The system is able to detect the next grid center which is useful if the system misses a trigger point. It's not perfect yet and you can see it does actually miss one prediction, but as long as it gets most of them, this should be ok.