AI on the bog: Monitoring and evaluating cranberry crop risk

TY - GEN

T1 - AI on the bog

T2 - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021

AU - Akiva, Peri

AU - Planche, Benjamin

AU - Roy, Aditi

AU - Dana, Kristin

AU - Oudemans, Peter

AU - Mars, Michael

N1 - Publisher Copyright: © 2021 IEEE.

PY - 2021/1

Y1 - 2021/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85109566857

UR - https://www.scopus.com/pages/publications/85109566857#tab=citedBy

U2 - 10.1109/WACV48630.2021.00254

DO - 10.1109/WACV48630.2021.00254

M3 - Conference contribution

T3 - Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021

SP - 2492

EP - 2501

BT - Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 5 January 2021 through 9 January 2021

ER -