Abstract
With the advent of multispectral imagery and AI, there have been numerous works on automatic plant segmentation for purposes such as counting, picking, health monitoring, localized pesticide delivery, etc. In this paper, we tackle the related problem of automatic and selective plant-clearing in a sustainable forestry context, where an autonomous machine has to detect and avoid specific plants while clearing any weeds which may compete with the species being cultivated. Such an autonomous system requires a high level of robustness to weather conditions, plant variability, terrain and weeds while remaining cheap and easy to maintain. We notably discuss the lack of robustness of spectral imagery, investigate the impact of the reference database's size and discuss issues specific to AI systems operating in uncontrolled environments.
Abstract (translated)
随着多光谱图像和人工智能（AI）的出现，已经出现了大量用于自动植物分割的应用，例如计数、采摘、健康监测、局部农药配送等。在本文中，我们研究了在可持续林业背景下，自动且选择性地清除植物的相关问题，其中一台自主机器需要检测和避开可能与被栽培的物种竞争的特定植物。这样的自主系统需要具备很高的适应性来应对天气条件、植物多样性、地形和杂草，同时保持低成本和易于维护。我们特别讨论了光谱图像的适应性不足，研究了参考数据库的大小，并讨论了运行在不受控环境中的AI系统所面临的问题。
URL
https://arxiv.org/abs/2404.13996
