Bright red cranberries are visible from space during the harvest season, which occurs from mid-September through mid-November in North America. These images show a sample of bog harvests in Wisconsin between 2015 and 2019 captured by the Landsat 8 and Sentinel-2 satellites. In 1959, a nationwide food panic erupted over a treasured Thanksgiving dish. Two weeks before the holiday, the federal government announced that cranberries had been contaminated by a cancer-causing chemical. Cranberry sales plummeted, schools tossed out cranberry products, restaurants eliminated the suspect fruit from menus.

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.

Ocean Spray Cranberry company has always used a human touch when interpreting data about its customers. The company developed its wildly popular Craisins product after discovering that only 38 percent of U.S. consumers were eating or drinking cranberries but around 60 percent of Americans like dried fruit. When social media became popular, Michael Nestrud, the company's senior manager of global sensory science and consumer affairs, made a hobby of analyzing Twitter posts about cranberries to get a sense of customers' emotions associated with the fruit. In many ways, Michael's approach is the wave of the future. However, unstructured customer data is unstructured and anecdotal -- only useful when you are able to sense patterns and incorporate the insights gained from them into customer outreach and experience.