AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk

Peri Akiva, Benjamin Planche, Aditi Roy, Kristin Dana, Peter Oudemans, Michael Mars; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 2493-2502

Machine vision for precision agriculture has attracted considerable research interest in recent years. The goal of this paper is to develop an end-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 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) when irradiance is predicted with 5.59-19.84% 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 in efficiency for farming and is useful in precision agriculture beyond the problem of cranberry overheating.