  Machine vision for precision agriculture has attracted considerable research
interest in recent years. The goal of this paper is to develop an end-to-end
cranberry health monitoring system to enable and support real time cranberry
over-heating assessment to facilitate informed decisions that may sustain the
economic viability of the farm. Toward this goal, we propose two main deep
learning-based modules for: 1) cranberry fruit segmentation to delineate the
exact fruit regions in the cranberry field image that are exposed to sun, 2)
prediction of cloud coverage conditions and sun irradiance to estimate the
inner temperature of exposed cranberries. We develop drone-based field data and
ground-based sky data collection systems to collect video imagery at multiple
time points for use in crop health analysis. Extensive evaluation on the data
set shows that it is possible to predict exposed fruit's inner temperature with
high accuracy (0.02% MAPE). The sun irradiance prediction error was found to be
8.41-20.36% MAPE in the 5-20 minutes time horizon. With 62.54% mIoU for
segmentation and 13.46 MAE for counting accuracies in exposed fruit
identification, this system is capable of giving informed feedback to growers
to take precautionary action (e.g. irrigation) in identified crop field regions
with higher risk of sunburn in the near future. Though this novel system is
applied for cranberry health monitoring, it represents a pioneering step
forward for efficient farming and is useful in precision agriculture beyond the
problem of cranberry overheating.

Computer Vision and Pattern Recognition;Artificial Intelligence