Root Localization and Cutting Technologies for Mechanized Cabbage Harvesting
Year: 2026
Type: article
Abstract: Accurate root localization and precision cutting are essential for mechanized cabbage harvesting, yet reliable linkage between perception and cutting remains difficult under field conditions. This review synthesized recent advances in cabbage root/stem localization, cutting mechanisms, sensor-assisted control, and integrated harvesting systems. The sensing technologies considered include RGB and RGB-D imaging, depth/LiDAR and ultrasonic sensing, and machine-state sensing using IMUs, displacement sensors, and encoders, with particular emphasis on multi-sensor fusion for cutter positioning and adaptive control. Relevant studies were identified through Scopus, Web of Science, and Google Scholar databases. A structured narrative review approach was adopted, and 55 studies were included in the technical analysis. Unlike previous reviews, focused mainly on harvesting machinery and mechanization, this review emphasizes the perception–localization–cutting–control–performance chain and how sensing results are converted into executable cutting actions. The synthesis shows that strong performance of individual detection or cutting components does not necessarily ensure reliable field harvesting. Major gaps include the limited field validation of closed-loop perception–localization–control–cutting systems, insufficient robustness to occlusion, soil adhesion, plant-posture variation, and uneven terrain, and the lack of standardized end-to-end evaluation protocols. Future research should prioritize multimodal sensing, closed-loop adaptive cutting, perception-to-actuation synchronization, and standardized end-to-end performance metrics.
Source: Sensors
DOI: 10.3390/s26227132
Institution: Chungnam National University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225775779