TY - GEN
T1 - A PLAN for Tackling the Locust Crisis in East Africa
T2 - 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
AU - Tabar, Maryam
AU - Gluck, Jared
AU - Goyal, Anchit
AU - Jiang, Fei
AU - Morr, Derek
AU - Kehs, Annalyse
AU - Lee, Dongwon
AU - Hughes, David P.
AU - Yadav, Amulya
N1 - Funding Information:
This work was in part supported by NSF award #1909702 and the Bill and Melinda Gates Foundation Grant #141840. We are also grateful for support from the UN-FAO.
Publisher Copyright:
© 2021 ACM.
PY - 2021/8/14
Y1 - 2021/8/14
N2 - East Africa is experiencing the worst locust infestation in over 25 years, which has severely threatened the food security of millions of people across the region. The primary strategy adopted by human experts at the United Nations Food and Agricultural Organization (UN-FAO) to tackle locust outbreaks involves manually surveying at-risk geographical areas, followed by allocating and spraying pesticides in affected regions. In order to augment and assist human experts at the UN-FAO in this task, we utilize crowdsourced reports of locust observations collected by PlantVillage (the world's leading knowledge delivery system for East African farmers) and develop PLAN, a Machine Learning (ML) algorithm for forecasting future migration patterns of locusts at high spatial and temporal resolution across East Africa. PLAN's novel spatio-temporal deep learning architecture enables representing PlantVillage's crowdsourced locust observation data using novel image-based feature representations, and its design is informed by several unique insights about this problem domain. Experimental results show that PLAN achieves superior predictive performance against several baseline models - it achieves an AUC score of 0.9 when used with a data augmentation method. PLAN represents a first step in using deep learning to assist and augment human expertise at PlantVillage (and UN-FAO) in locust prediction, and its real-world usability is currently being evaluated by domain experts (including a potential idea to use the heatmaps created by PLAN in a Kenyan TV show). The source code is available at https://github.com/maryam-tabar/PLAN.
AB - East Africa is experiencing the worst locust infestation in over 25 years, which has severely threatened the food security of millions of people across the region. The primary strategy adopted by human experts at the United Nations Food and Agricultural Organization (UN-FAO) to tackle locust outbreaks involves manually surveying at-risk geographical areas, followed by allocating and spraying pesticides in affected regions. In order to augment and assist human experts at the UN-FAO in this task, we utilize crowdsourced reports of locust observations collected by PlantVillage (the world's leading knowledge delivery system for East African farmers) and develop PLAN, a Machine Learning (ML) algorithm for forecasting future migration patterns of locusts at high spatial and temporal resolution across East Africa. PLAN's novel spatio-temporal deep learning architecture enables representing PlantVillage's crowdsourced locust observation data using novel image-based feature representations, and its design is informed by several unique insights about this problem domain. Experimental results show that PLAN achieves superior predictive performance against several baseline models - it achieves an AUC score of 0.9 when used with a data augmentation method. PLAN represents a first step in using deep learning to assist and augment human expertise at PlantVillage (and UN-FAO) in locust prediction, and its real-world usability is currently being evaluated by domain experts (including a potential idea to use the heatmaps created by PLAN in a Kenyan TV show). The source code is available at https://github.com/maryam-tabar/PLAN.
UR - http://www.scopus.com/inward/record.url?scp=85114917272&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114917272&partnerID=8YFLogxK
U2 - 10.1145/3447548.3467184
DO - 10.1145/3447548.3467184
M3 - Conference contribution
AN - SCOPUS:85114917272
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 3595
EP - 3604
BT - KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 14 August 2021 through 18 August 2021
ER -