  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.
