Abstract: Embodiments of the disclosed technologies are capable of inputting, to a machine-learned model that has been trained to recognize a horticultural product in digital imagery, digital video data comprising frames that represent a view of the horticultural product in belt-assisted transit from a picking area of a field to a harvester bin; outputting, by the machine-learned model, annotated video data; using the annotated video data, computing quantitative data comprising particular counts of the individual instances of the horticultural product associated with particular timestamp data; using the timestamp data, mapping the quantitative data to geographic location data to produce a digital yield map; causing display of the digital yield map on a field manager computing device.

Abstract: Embodiments of the disclosed technologies are capable of inputting, to a machine-learned classifier that has been created using a set of neural network-based models, multi-band digital image data that represents an aerial view of an agricultural field location containing an horticultural product; outputting, by the classifier, annotated image data, the annotated image data comprising annotation data indicative of individual instances of the horticultural product in the agricultural field location; using the annotated image data, computing a first predicted yield for the agricultural field location; adjusting the first predicted yield by a scaling factor to produce a second predicted yield for the agricultural field location.

Abstract: In an embodiment, a computer-implemented method of calibrating an imaging system in real-time, comprising: obtaining a first reading by a first sensor; establishing a dynamic link between the first reading and exposure time of a second sensor; using the dynamic link to control the exposure time of the second sensor; obtaining a second reading by the second sensor during the controlled exposure time; wherein the steps are performed by one or more computing devices.

Abstract: In an embodiment, a computer-implemented method of calibrating an imaging system in real-time, comprising: obtaining a first reading by a first sensor; establishing a dynamic link between the first reading and exposure time of a second sensor; using the dynamic link to control the exposure time of the second sensor; obtaining a second reading by the second sensor during the controlled exposure time; wherein the steps are performed by one or more computing devices.

Abstract: In an embodiment, a computer-implemented method of calibrating an imaging system in real-time, comprising: obtaining a first reading by a first sensor; establishing a dynamic link between the first reading and exposure time of a second sensor; using the dynamic link to control the exposure time of the second sensor; obtaining a second reading by the second sensor during the controlled exposure time; wherein the steps are performed by one or more computing devices.

Abstract: Embodiments of the disclosed technologies are capable of inputting, to a machine-learned classifier that has been created using a set of neural network-based models, multi-band digital image data that represents an aerial view of an agricultural field location containing an horticultural product; outputting, by the classifier, annotated image data, the annotated image data comprising annotation data indicative of individual instances of the horticultural product in the agricultural field location; using the annotated image data, computing a first predicted yield for the agricultural field location; adjusting the first predicted yield by a scaling factor to produce a second predicted yield for the agricultural field location.

Abstract: Embodiments of the disclosed technologies are capable of inputting, to a machine-learned model that has been trained to recognize a horticultural product in digital imagery, digital video data comprising frames that represent a view of the horticultural product in belt-assisted transit from a picking area of a field to a harvester bin; outputting, by the machine-learned model, annotated video data; using the annotated video data, computing quantitative data comprising particular counts of the individual instances of the horticultural product associated with particular timestamp data; using the timestamp data, mapping the quantitative data to geographic location data to produce a digital yield map; causing display of the digital yield map on a field manager computing device.

Abstract: In an embodiment, a computer-implemented method of calibrating an imaging system in real-time, comprising: obtaining a first reading by a first sensor; establishing a dynamic link between the first reading and exposure time of a second sensor; using the dynamic link to control the exposure time of the second sensor; obtaining a second reading by the second sensor during the controlled exposure time; wherein the steps are performed by one or more computing devices.