A computer-implemented method implemented using one or more processors for predicting crop yield, comprising: obtaining a first temporal sequence of high-elevation digital images, wherein the first temporal sequence of high-elevation digital images capture a first geographic area and are acquired over a first predetermined time interval, and wherein during the first predetermined time interval, the first geographic area includes a first type of crop; obtaining a first plurality of other data points, wherein the first plurality of other data points comprise crop-influencing and observational data points that influence a ground truth crop yield of the first geographic area after the first predetermined time interval, the first plurality of other data points comprising at least one of: operational data generated by farm machinery used to manage the first geographic area and operational data relating to human-induced factors influencing the crop yields of the first geographic area, and wherein the first plurality of other data points are grouped into temporal chunks, each temporal chunk of the first plurality of other data points corresponding temporally with a respective high-elevation digital image of the first temporal sequence of high-elevation digital images; iteratively applying the high-elevation digital images of the first temporal sequence and the temporal chunks of the first plurality of other data points as input across a trained recurrent neural network to generate first output indicative of an estimated crop yield of the first geographic area at the end of the first predetermined time interval; and causing one or more output components of one or more computing devices to audibly or visually convey the estimated crop yield of the first geographic area at the end of the first predetermined time interval. obtaining a first temporal sequence of high-elevation digital images, wherein the first temporal sequence of high-elevation digital images capture a first geographic area and are acquired over a first predetermined time interval, and wherein during the first predetermined time interval, the first geographic area includes a first type of crop; obtaining a first plurality of other data points, wherein the first plurality of other data points comprise crop-influencing and observational data points that influence a ground truth crop yield of the first geographic area after the first predetermined time interval, the first plurality of other data points comprising at least one of: operational data generated by farm machinery used to manage the first geographic area and operational data relating to human-induced factors influencing the crop yields of the first geographic area, and wherein the first plurality of other data points are grouped into temporal chunks, each temporal chunk of the first plurality of other data points corresponding temporally with a respective high-elevation digital image of the first temporal sequence of high-elevation digital images; iteratively applying the high-elevation digital images of the first temporal sequence and the temporal chunks of the first plurality of other data points as input across a trained recurrent neural network to generate first output indicative of an estimated crop yield of the first geographic area at the end of the first predetermined time interval; and causing one or more output components of one or more computing devices to audibly or visually convey the estimated crop yield of the first geographic area at the end of the first predetermined time interval. <EOS>