Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers. In this work, we present a deep learning method for simultaneous segmentation and counting of cranberries to aid in yield estimation and sun exposure predictions. Notably, supervision is done using low cost center point annotations. The approach, named Triple-S Network, incorporates a three-part loss with shape priors to promote better fitting to objects of known shape typical in agricultural scenes. Our results improve overall segmentation performance by more than 6.74% and counting results by 22.91% when compared to state-of-the-art. To train and evaluate the network, we have collected the CRanberry Aerial Imagery Dataset (CRAID), the largest dataset of aerial drone imagery from cranberry fields. This dataset will be made publicly available.

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.

Cranberry fruit rot (CFR) has constrained commercial cranberry production since the mid-1800s and remains among the most economically significant diseases affecting the crop. Historically viewed as a disease caused by a limited number of fungal pathogens, CFR is now recognized as a complex, multiscale pathosystem involving diverse fungal taxa, latent infections, host developmental stage, and environmentally driven disease expression. Approximately 12–15 fungal species have been associated with the fruit rot complex, although the ecological roles, infection biology, and relative contributions of individual taxa remain incompletely resolved. Recent advances in molecular diagnostics, including quantitative PCR and next-generation sequencing approaches, have expanded understanding of CFR pathogen diversity and revealed that many associated fungi occur asymptomatically during early fruit development before increasing in abundance as fruit mature. These findings challenge traditional assumptions regarding infection timing and disease progression and suggest that CFR development is influenced by interactions among pathogen communities, host physiology, and canopy microclimate. Current management of CFR relies heavily on prophylactic fungicide applications initiated during bloom; however, inconsistent efficacy, fungicide resistance concerns, regulatory restrictions, and increasing production costs highlight the limitations of fungicide-dependent approaches. Increasing evidence suggests that canopy architecture, humidity, fruit temperature, and other environmental variables influence disease severity and pathogen composition, emphasizing the need for integrated disease management strategies incorporating cultural practices, environmental monitoring, and improved forecasting tools. This review synthesizes historical and contemporary understanding of CFR biology, pathogen diversity, epidemiology, diagnostics, and management while identifying key knowledge gaps and future research priorities needed to support sustainable, regionally adapted disease management strategies for cranberry production systems.