A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa
<|reference_start|>A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa: 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.<|reference_end|>
@article{yusuf2024a,
    title={A Geospatial Approach to Predicting Desert Locust Breeding Grounds in
  Africa},
    author={Ibrahim Salihu Yusuf, Mukhtar Opeyemi Yusuf, Kobby Panford-Quainoo,
  Arnu Pretorius},
    journal={arXiv preprint arXiv:2403.06860},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06860},
    primaryClass={cs.LG cs.CV}
}