Netflora, developed by Embrapa, represents a significant advancement in forest management through the use of artificial intelligence (AI). This methodology utilizes a set of algorithms specifically trained to enhance AI forest species identification.
With a notable accuracy rate of 95%, these algorithms can identify key species like chestnut, cumaru-ferro, açaí, and cedar, which not only streamlines the management process but also significantly reduces operational costs. The transition from traditional methods to AI forest species identification has led to a reduction in the cost of forest mapping from approximately R$140 per hectare to just R$6, showcasing the cost-efficiency of AI in large-scale environmental projects.
The AI forest species identification technology leverages data from over 40,000 hectares of surveyed forest areas in Acre, Rondônia, and Southern Amazonas, utilizing drones to gather the necessary data. This extensive dataset has been instrumental in training the algorithms, ensuring that they can effectively distinguish between different forest species and contribute to more sustainable forest management practices. Embrapa aims to expand this initiative to cover 80,000 hectares, including new commercial areas of interest in the Amazon, demonstrating the scalability and impact of this AI-driven approach.
Netflora’s approach to AI forest species identification does more than just identify tree species; it enhances the entire ecosystem of forest management. By automating the planning and execution of forestry activities, it introduces greater precision and efficiency. The algorithms are trained not only to recognize species but also to provide detailed metrics such as tree diameter and crown area. These measurements are crucial for estimating the volume of wood available in each tree through allometric equations, integrating environmental conservation with increased forestry production.
The broader implications of this technology extend to reducing the labor and time required for traditional forest surveys. For instance, a forestry company using conventional methods might map up to 10,000 hectares per year, but with the adoption of Netflora’s AI tools, the capacity could increase to one million hectares annually. This tremendous increase in operational efficiency not only makes the process more cost-effective but also significantly faster, enabling rapid scaling and adaptation to various forestry management needs.
Moreover, Netflora is poised to revolutionize the forestry sector further with the upcoming launch of new algorithms. These will not only enhance the identification and mapping of forest species but also facilitate environmental monitoring and management. The methodology is publicly accessible, hosted on GitHub, and can be easily deployed using tools like Google Colab, making it accessible to a broad audience including forestry companies, academic institutions, and environmental organizations.
Overall, the integration of AI in forest management through the Netflora methodology highlights a transformative shift in how forest resources are mapped, analyzed, and managed. By harnessing the power of AI forest species identification, Embrapa is not only improving the accuracy and efficiency of forest management but also contributing to the sustainability of forest ecosystems in the Amazon. This innovative approach not only supports the ecological and economic aspects of forestry but also sets a new standard in the application of technology for environmental conservation.