Application of Artificial Intelligence in Tree Care in Sub-Saharan Africa

• Chavula, Petros ^[1] ; Kayusi, Fredrick ^[2] ; Agura Kayus, Bismark ^[3]
  1. [1] World Agroforestry Centre

     World Agroforestry Centre

     Zambia

     
  2. [2] Maasai Mara University

     Maasai Mara University

     Kenia

     
  3. [3] Kisii University, Department of Curriculum, Instructions & Media, School of Education & Human Resource Development, P.O. Box 408-40200, Kisii-Kenya.

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• Localización: LatIA, ISSN-e 3046-403X, Nº. 3, 2025 (Ejemplar dedicado a: LatIA)
• Idioma: inglés
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• Resumen

  • Artificial intelligence (AI) has emerged as a transformative tool in various industries, including environmental conservation and tree care. In Sub-Saharan Africa, where deforestation, climate change, and inadequate tree management pose significant challenges, AI presents opportunities for improving tree care practices. This study explores the application of AI technologies in tree monitoring, disease detection, and sustainable management strategies within the region. Utilizing a combination of literature review and case study analysis, the research evaluates AI-driven approaches such as remote sensing, machine learning models, and automated data collection for assessing tree care and forest dynamicos. The findings indicate that AI enhances early disease detection, optimizes resource allocation, and supports decision-making for conservation efforts. However, challenges such as limited technological infrastructure, high implementation costs, and the need for specialized expertise hinder widespread adoption. The study concludes that while AI holds significant potential for revolutionizing tree care in Sub-Saharan Africa, strategic investments in digital infrastructure, policy support, and capacity building are essential for its successful integration into forestry and environmental management practices.