Robust pixel-based classification of obstacles for robotic harvesting of sweet-pepper
First study with quantified performance of obstacle classification for harvesting. Classification by two approaches, each comprising four classification problems. Pixel classification of plant parts under varying light, in a greenhouse, is limited. True- and false-positive detection rate of hard obstacles limited to 59.2% and 8.5%. A new robust-and-balanced accuracy performance measure PRob successfully introduced.
plant parts harvesting robots foliage detection cart robust classification multi-spectral imaging