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Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3–4, 2024, Proceedings

Research Article

Agricultural and Land Management Using AI: A Case Study of Rice Plot Identification in Senegal

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  • @INPROCEEDINGS{10.1007/978-3-031-86493-3_24,
        author={Mariama Drame and Seydina Moussa Ndiaye and Moussa Lo},
        title={Agricultural and Land Management Using AI: A Case Study of Rice Plot Identification in Senegal},
        proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3--4, 2024, Proceedings},
        proceedings_a={INTERSOL},
        year={2025},
        month={4},
        keywords={Agriculture AI Machine Learning Remote sensing Crop type mapping},
        doi={10.1007/978-3-031-86493-3_24}
    }
    
  • Mariama Drame
    Seydina Moussa Ndiaye
    Moussa Lo
    Year: 2025
    Agricultural and Land Management Using AI: A Case Study of Rice Plot Identification in Senegal
    INTERSOL
    Springer
    DOI: 10.1007/978-3-031-86493-3_24
Mariama Drame,*, Seydina Moussa Ndiaye, Moussa Lo
    *Contact email: mariama.drame@unchk.edu.sn

    Abstract

    Agricultural yield improvement is important to handle food insecurity mostly for developing countries. Indeed accurate knowledge of the distribution of crops in the landscape is crucial for better management and monitoring of the agricultural sector. In recent years the combination of artificial intelligence (AI) and remote sensing data has been widely used for crop type mapping. Given the essential place of rice in the Senegalese diet, increasing its production can positively impact food security. Thus, having an estimate of its harvests can be useful to stakeholders for better management of the rice sector in Senegal. In this work, we aim to build an AI system with remote sensing data for rice crop mapping in Senegal. We exploit two AI models with Sentinel-2 images for rice mapping. The first model is based on Support Vector Machine (SVM) and a second model based on deep learning using the Deeplab V3+ model. Both models shows promising results even if they still very low. The results reveals that the deep learning model provides better performance at identifying rice crop than the SVM model which has a lower accuracy.

    Keywords
    Agriculture AI Machine Learning Remote sensing Crop type mapping
    Published
    2025-04-21
    Appears in
    SpringerLink
    http://dx.doi.org/10.1007/978-3-031-86493-3_24
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