I-Corps: Automating reforestation using machine learning and unmanned aerial vehicles

这个i-Corps项目的更广泛的影响/商业潜力是通过空中重新造林为改善森林资源管理提供更强的能力。可持续的森林管理做法对于减少土壤侵蚀、增加生物多样性、封存碳以及满足未来对纸浆、造纸、木材和能源部门的木材和生物质供应的需求十分重要。对已清理或退化的土地进行再造林(自然或辅助)是实现这些目标的一个重要管理目标。私人林地的管理成本随着时间的推移而增加，特别是对小规模的私人土地所有者而言。配备定制硬件和软件的无人驾驶飞行器能够取代或补充传统的辅助植树造林做法(手工种植或机械种植)，并提供具有成本效益的替代办法。这个项目的重新造林方法可以增加对物理偏远或地形受限的地点的可及性，这些地点很难用大型重型机械和车辆到达。这个i-Corps项目将探索用于辅助重新造林工作的数据驱动管理系统的商业可行性。该系统旨在使用配备了定制硬件的无人机播种。该系统硬件能够将定制的种子颗粒投射到一系列土壤条件中，并达到成功萌发所需的渗透深度。与该系统相结合的是一个机器学习算法，它使用遥感图像提供关于林地所有权的数据指标。机器学习算法可以根据数据类型和数量进行训练，以便进行更准确和可靠的分析。这些数据使私人土地所有者能够评估其所持土地的整体状况，促进重新造林计划的制定，并确定飞行模式。此外，该算法还能够通信和引导无人机的自主导航系统指导田间种植。

The broader impact/commercial potential of this I-Corps project is to provide enhanced capabilities for improved management of forest resources via aerial reforestation. Sustainable forest management practices are important to decrease soil erosion, increase biodiversity, sequester carbon and meet future demand for timber and biomass supply to the pulp, paper, wood, and energy sectors. Reforestation (natural or assisted) of cleared or degraded land is an important management objective to achieve these goals. Management costs of private forest lands have increased over time particularly for small-scale private landowners. Unmanned aerial vehicles, equipped with customized hardware and software, have the ability to replace or supplement conventional assisted-reforestation practices (hand planting or mechanical) and provide a cost-effective alternative. This project's approach to reforestation can increase accessibility to physically remote or topographically constrained locations that are difficult to access with large heavy machinery and vehicles.This I-Corps project will explore the commercial viability of a data driven management system for assisted reforestation efforts. The system is designed to plant seeds using unmanned aerial vehicles outfitted with customized hardware. The system hardware is capable of projecting customize seed pellets into a range of soil conditions and at penetration depths necessary for successful germination. Coupled to the system is a machine learning algorithm that provides data metrics on forest land holdings using remotely sensed images. The machine learning algorithm can be trained, based on data type and quantity, for more accurate and reliable analysis. These data enable private landowners to assess overall conditions of their holdings, facilitate development of reforestation plans, and determine flight patterns. In addition, the algortihm is capable of communicating and guiding the unmanned aerial vehicle's autonomous guidance system to direct field planting.