Table 5.

Phenotyping agrirobotic systems for various crops.

Crop	Perception	Autonomy Level	Results	Cited Work
Maize & wheat	Cameras, spectral imaging systems, laser sensors, 3D time-of-flight cameras	Autonomous	No performance metrics provided	[75]
Cotton	Stereo RGB & IR thermal camera, temperature, humidity, light intensity sensors, pyranometer, quantum, LiDAR	Semi-autonomous	RMS error: Plant height: < 0.5 cm (Vinobot) RGB to IR calibration: 2.5 px (Vinoculer) Temp: < 1 °C (Vinoculer)	[78]
Sorghum	Stereo camera, RGB camera with fish eye lenses, penetrometer	Autonomous	Stalk detection: 96%	[73]
Rice, maize & wheat	RGB camera, chlorophyll fluorescence camera, NDVI camera, thermal infrared camera, hyperspectral camera, 3D laser scanner	Fixed site fully automated	Plant height RMS error: 1.88 cm	[76]
Sugar Beet	Mobile robot: Webcam camera, gigaethernet camera Bettybot: Color camera, hyperspectral camera	Autonomous	No performance metrics provided	[74]
Sorghum	Stereo imaging system consisting of color cameras	Autonomous based on commercial tractor	The image-derived measurements were highly repeatable & showed high correlations with manual measurements.	[77]
Energy Sorghum	Stereo camera, time of flight depth sensor, IR camera	Semi-autonomous	Average absolute error for stem width and plant height: 13% and 15%.	[79]