AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk Summer 2020
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 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 (\eg, 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.
H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement Summer 2020
Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information. Instruments and sensors useful for flood detection are rarely available in low resolution, low latency satellites with region re-visit periods of up to 16 days, making flood alerting systems that use such satellite unreliable. Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information. Instruments and sensors useful for flood detection are rarely available in low resolution, low latency satellites with region re-visit periods of up to 16 days, making flood alerting systems that use such satellite unreliable. This work presents H2O-Network, a self supervised deep learning method to segment floods from satellites and aerial imagery by bridging domain gap between low and high latency satellite and coarse-to-fine label refinement. H2O-Net learns to synthesize signals highly correlative with water presence as a domain adaptation step for semantic segmentation in high resolution satellite imagery. Our work also proposes a self-supervision mechanism, which does not require any hand annotation, used during training to generate high quality ground truth data. We demonstrate that H2O-Net outperforms the state-of-the-art semantic segmentation methods on satellite imagery by 16.43% for the task of flood segmentation. We also show that our method may be beneficial to other domains that make use of reflectance properties.
Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors Spring 2020
CVPRW, 2020 (Oral Presentation)
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.