Summary of A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa, by Ibrahim Salihu Yusuf et al.
A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa
by Ibrahim Salihu Yusuf, Mukhtar Opeyemi Yusuf, Kobby Panford-Quainoo, Arnu Pretorius
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a machine learning model that predicts desert locust breeding grounds, which can enhance early warning systems and targeted control measures. The model uses custom deep learning models, including three-dimensional and LSTM-based recurrent convolutional networks, along with the geospatial foundational model Prithvi. The approach is tested on a dataset curated from the United Nations Food and Agriculture Organization’s (UN-FAO) locust observation records, using remotely-sensed environmental and climate data as well as multi-spectral earth observation images. The results show that the Prithvi-based model, fine-tuned on NASA’s Harmonized Landsat and Sentinel-2 (HLS) dataset, achieves high accuracy, F1, and ROC-AUC scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Desert locust swarms are a major problem for agriculture and food security. This paper helps solve this issue by creating a model that predicts where these locusts will breed. The model uses special types of computer learning, like three-dimensional and recurrent convolutional networks, along with other tools to analyze data from the United Nations Food and Agriculture Organization (UN-FAO). The results show that this approach is very effective in predicting where locusts will breed. |
Keywords
* Artificial intelligence * Auc * Deep learning * Lstm * Machine learning
