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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors

February 1, 2023

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Authors

Maryam Tabar

The Pennsylvania State University


Dongwon Lee

The Pennsylvania State University


David P. Hughes

The Pennsylvania State University


Amulya Yadav

The Pennsylvania State University


DOI:

10.1609/aaai.v36i11.21534


Abstract:

African smallholder farmers have struggled with low agricultural productivity for decades, partly due to their inability to proactively assess irrigation needs in their farms in the face of long-term climate change. In this paper, we tackle this challenge by employing data-driven techniques to develop forecasting tools for three widely used crop-productivity related variables (i.e., actual evapotranspiration, reference evapotranspiration, and net primary production), which can then be used by farmers to take corrective actions on their farms. Prior work in this domain, despite using data-driven methods, suffers from two major limitations: (i) they mainly focus on estimating variable values (as opposed to forecasting the future); and (ii) they mostly use classical Machine Learning (ML) prediction models, despite the abundance of data sufficient to train sophisticated deep learning models. To fill this research gap, we collaborate with PlantVillage, the world’s leading non-profit agricultural knowledge delivery platform for African farmers, to identify ∼2,200 smallholder farm locations, and gather remote-sensed data of these farms over a period of five years. Next, we propose CLIMATES, a meta-algorithm leveraging structural insights about temporal patterns of this time-series data to accurately forecast their future values. We conduct extensive experiments to evaluate its performance in this domain. Our experimental results show that CLIMATES outperforms several state-of-the-art time-series forecasting models. We also provide insights about the poor performance of some competing models. Our work is being evaluated by officials at PlantVillage for potential future deployment as an early warning system in East Africa. We release the code at https://github.com/maryam-tabar/CLIMATES.

Topics: AAAI

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HOW TO CITE:

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors Proceedings of the AAAI Conference on Artificial Intelligence (2022) 12608-12614.

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors AAAI 2022, 12608-12614.

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav (2022). Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors. Proceedings of the AAAI Conference on Artificial Intelligence, 12608-12614.

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav. Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.12608-12614.

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav. 2022. Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors. "Proceedings of the AAAI Conference on Artificial Intelligence". 12608-12614.

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav. (2022) "Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors", Proceedings of the AAAI Conference on Artificial Intelligence, p.12608-12614

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav, "Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors", AAAI, p.12608-12614, 2022.

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav. "Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.12608-12614.

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav. "Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 12608-12614.

Maryam Tabar||Dongwon Lee||David P. Hughes||Amulya Yadav. Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors. AAAI[Internet]. 2022[cited 2023]; 12608-12614.


ISSN: 2374-3468


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