Sugarcane Smart Planting Visualization

An autonomous farming machine comprising:

• one or more processors physically attached to the autonomous farming machine;
• and
• a non-transitory computer readable storage medium storing computer program instructions that, when executed by the one or more processors, cause the one or more processors to:
• access a target planting ratio quantifying a target ratio of billets to soil in a furrow over a distance;
• access, in real-time from a camera of a sugarcane planting machine as the sugarcane planting machine performs a planting routine in a field, image data representing the furrow in the field, and the image data comprising pixels representing billets and soil in the furrow;
• apply a machine-learned model to the image data, the machine-learned model configured to:
• for each pixel in the image data, classify the pixel as billets or soil, and
• calculate a current planting ratio using the image data classified as billets or soil, the current planting ratio quantifying a current ratio of billets to soil in the furrow over the distance;
• generate a visualization for a user interface, wherein the visualization comprises the classified image data captured by the camera of the sugarcane planting machine, a time series of the current planting ratio, and a comparison of the current planting ratio and the target planting ratio;
• and
• display the user interface on a display of the sugarcane planting machine.

A non-transitory computer readable storage medium storing computer program instructions that, when executed by one or more processors, cause the one or more processors to:

• access a target planting ratio quantifying a target ratio of billets to soil in a furrow over a distance;
• access, in real-time from a camera of a sugarcane planting machine as the sugarcane planting machine performs a planting routine in a field, image data representing the furrow in the field, and the image data comprising pixels representing billets and soil in the furrow;
• apply a machine-learned model to the image data, the machine-learned model configured to:
• for each pixel in the image data, classify the pixel as billets or soil, and
• calculate a current planting ratio using the image data classified as billets or soil, the current planting ratio quantifying a current ratio of billets to soil in the furrow over the distance;
• generate a visualization for a user interface, wherein the visualization comprises the classified image data captured by the camera of the sugarcane planting machine, a time series of the current planting ratio, and a comparison of the current planting ratio and the target planting ratio;
• and
• display the user interface on a display of the sugarcane planting machine.