A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa

• URL: http://arxiv.org/abs/2403.06860v2
• Date: Thu, 21 Mar 2024 17:06:49 GMT
• Title: A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa
• Authors: Ibrahim Salihu Yusuf, Mukhtar Opeyemi Yusuf, Kobby Panford-Quainoo, Arnu Pretorius,
• Abstract summary: locust swarms present a major threat to agriculture and food security. Our study develops an operationally-ready model for predicting locust breeding grounds.
• Score: 3.6826233660285395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Desert locust swarms present a major threat to agriculture and food security. Addressing this challenge, our study develops an operationally-ready model for predicting locust breeding grounds, which has the potential to enhance early warning systems and targeted control measures. We curated a dataset from the United Nations Food and Agriculture Organization's (UN-FAO) locust observation records and analyzed it using two types of spatio-temporal input features: remotely-sensed environmental and climate data as well as multi-spectral earth observation images. Our approach employed custom deep learning models (three-dimensional and LSTM-based recurrent convolutional networks), along with the geospatial foundational model Prithvi recently released by Jakubik et al., 2023. These models notably outperformed existing baselines, with the Prithvi-based model, fine-tuned on multi-spectral images from NASA's Harmonized Landsat and Sentinel-2 (HLS) dataset, achieving the highest accuracy, F1 and ROC-AUC scores (83.03%, 81.53% and 87.69%, respectively). A significant finding from our research is that multi-spectral earth observation images alone are sufficient for effective locust breeding ground prediction without the need to explicitly incorporate climatic or environmental features.

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