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Smart forestry (SF) is a novel approach that integrates progressive approaches such as big data, artificial intelligence (AI), internet of things (IoT), and remote sensing (RS) to enhance forest resource management (FRM) while reducing the environmental impact. The integration of these advanced technologies into traditional forestry enables to make predictions and optimal decisions automatically for sustainable forestry management. The effectiveness of these approaches in this field is dependent upon the accessibility of high-quality data, illustrating the significant role played by sensing techniques in the smart revolution of forestry. But, to achieve the complete efficiency of SF demands incorporation of different approaches including IoT, AI, big data, etc., throughout the process chain for precise resource management in forestry. This document explores the smart approaches used in digital transformation of forestry, particularly the technologies used in resource management. These advanced technologies allow to gather real-time data from forest, automatic data analysis and predictive decision-making, which reduces manual burden and provides better resource management. In smart forestry, the remote sensing technologies enables to identify and categorize species, monitors tree health, heat stress, water stress, and detects illegal logging. In addition, AI-based model integrated with data acquired from sensing technologies provide automatic decision-making regarding forest fire prediction, climate forecasting, and rainfall analysis in the region. This predictive technology helps in preserving forest resources from natural calamities and man-made disasters through early warning and timely intervention. Moreover, the sensing approaches provide continuous monitoring, fungi prediction, pest control and disease classification, protecting forest resources from damage. This document explores the digital transformation of forestry by analyzing these smart applications and assessing their effectiveness in improving forest management.

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Smart Approaches to Forestry: Techniques and Tools for Precision Resource Management

  • 章节

  • 发表日期：

    2026年

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  • Jahanara Begum,
  • Gattu Shravani,
  • T. Saravanan,
  • R. Hemavathi,
  • Gayatri Parasa

摘要

Smart forestry (SF) is a novel approach that integrates progressive approaches such as big data, artificial intelligence (AI), internet of things (IoT), and remote sensing (RS) to enhance forest resource management (FRM) while reducing the environmental impact. The integration of these advanced technologies into traditional forestry enables to make predictions and optimal decisions automatically for sustainable forestry management. The effectiveness of these approaches in this field is dependent upon the accessibility of high-quality data, illustrating the significant role played by sensing techniques in the smart revolution of forestry. But, to achieve the complete efficiency of SF demands incorporation of different approaches including IoT, AI, big data, etc., throughout the process chain for precise resource management in forestry. This document explores the smart approaches used in digital transformation of forestry, particularly the technologies used in resource management. These advanced technologies allow to gather real-time data from forest, automatic data analysis and predictive decision-making, which reduces manual burden and provides better resource management. In smart forestry, the remote sensing technologies enables to identify and categorize species, monitors tree health, heat stress, water stress, and detects illegal logging. In addition, AI-based model integrated with data acquired from sensing technologies provide automatic decision-making regarding forest fire prediction, climate forecasting, and rainfall analysis in the region. This predictive technology helps in preserving forest resources from natural calamities and man-made disasters through early warning and timely intervention. Moreover, the sensing approaches provide continuous monitoring, fungi prediction, pest control and disease classification, protecting forest resources from damage. This document explores the digital transformation of forestry by analyzing these smart applications and assessing their effectiveness in improving forest management.