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<p>Sugarcane is a vital agricultural crop in Brazil, playing a crucial role in both the national economy and bioenergy production. To enhance productivity and operational efficiency across the sugarcane production chain, the adoption of advanced technologies, such as machine learning (ML) algorithms, has become increasingly essential. This study aims to assess the current state of scientific research on the application of ML algorithms in Brazilian sugarcane cultivation through a comprehensive review of national and international publications. The findings reveal a predominance of supervised learning algorithms, particularly Random Forest, Support Vector Machine, Decision Tree, and Artificial Neural Networks. In contrast, unsupervised learning methods have been applied less frequently, while reinforcement learning techniques are notably absent from the reviewed studies. The analysis also highlights significant regional disparities, with a strong concentration of research in the Southeast region, Brazil’s primary sugarcane-producing area. Meanwhile, the North region—despite its considerable potential for expansion—exhibits limited research activity. This review underscores the need to explore underutilized ML approaches, such as semi-supervised and reinforcement learning, to better address data scarcity and the complexities of dynamic agricultural management. Additionally, the incorporation of Explainable Artificial Intelligence techniques, such as SHAP and LIME, is recommended to enhance model transparency and interpretability for decision-makers. These findings highlight the importance of expanding both methodological diversity and geographical coverage in Brazilian sugarcane research while encouraging the integration of emerging ML techniques to foster sustainable agricultural practices and improve crop productivity.</p>

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Use of machine learning algorithms in the context of sugarcane in Brazil: a review

  • 期刊论文

  • 发表日期：

    2025年3月18日

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  • Luiz Antonio Soares Cardoso,
  • Brehme D’napoli Reis de Mesquita,
  • Paulo Roberto Silva Farias

摘要

Sugarcane is a vital agricultural crop in Brazil, playing a crucial role in both the national economy and bioenergy production. To enhance productivity and operational efficiency across the sugarcane production chain, the adoption of advanced technologies, such as machine learning (ML) algorithms, has become increasingly essential. This study aims to assess the current state of scientific research on the application of ML algorithms in Brazilian sugarcane cultivation through a comprehensive review of national and international publications. The findings reveal a predominance of supervised learning algorithms, particularly Random Forest, Support Vector Machine, Decision Tree, and Artificial Neural Networks. In contrast, unsupervised learning methods have been applied less frequently, while reinforcement learning techniques are notably absent from the reviewed studies. The analysis also highlights significant regional disparities, with a strong concentration of research in the Southeast region, Brazil’s primary sugarcane-producing area. Meanwhile, the North region—despite its considerable potential for expansion—exhibits limited research activity. This review underscores the need to explore underutilized ML approaches, such as semi-supervised and reinforcement learning, to better address data scarcity and the complexities of dynamic agricultural management. Additionally, the incorporation of Explainable Artificial Intelligence techniques, such as SHAP and LIME, is recommended to enhance model transparency and interpretability for decision-makers. These findings highlight the importance of expanding both methodological diversity and geographical coverage in Brazilian sugarcane research while encouraging the integration of emerging ML techniques to foster sustainable agricultural practices and improve crop productivity.