精密農業における除草剤散布のための実用的物体検出に向けて
Towards practical object detection for weed spraying in precision agriculture
精密農業での除草剤散布に使う物体検出を、精度だけでなく実運用の観点から評価する3つの指標を提案し、実験で有用性を示した論文。
著者: Adrian Salazar-Gomez, Madeleine Darbyshire, Junfeng Gao, Elizabeth I Sklar, Simon Parsons
分類: cs.CV, cs.RO
原文アブストラクト
The evolution of smaller, faster processors and cheaper digital storage mechanisms across the last 4-5 decades has vastly increased the opportunity to integrate intelligent technologies in a wide range of practical environments to address a broad spectrum of tasks. One exciting application domain for such technologies is precision agriculture, where the ability to integrate on-board machine vision with data-driven actuation means that farmers can make decisions about crop care and harvesting at the level of the individual plant rather than the whole field. This makes sense both economically and environmentally. However, the key driver for this capability is fast and robust machine vision -- typically driven by machine learning (ML) solutions and dependent on accurate modelling. One critical challenge is that the bulk of ML-based vision research considers only metrics that evaluate the accuracy of object detection and do not assess practical factors. This paper introduces three metrics that highlight different aspects relevant for real-world deployment of precision weeding and demonstrates their utility through experimental results.
