Real-time robotic weed knife control system for tomato and lettuce based on geometric appearance of plant labels

Automated weed management tools in vegetable crops are needed to reduce or eliminate hand-weeding because of labour shortages and cost. Distinguishing crop plants from weeds in complex natural scenes of crop-weed mixtures remains a challenge for weed management automation. This paper presents a novel solution to the weed control problem by employing crop signalling technology: a novel systems approach that creates a machine-readable crop plant. A robot-vision-based weed-knife control system with a novel three-dimensional geometric detection algorithm was developed to automate weed control for tomato and lettuce crops. The system successfully detected the crop signal from occluded crop plants while traveling at speeds up to of 3.2 km h(-1). The in-field experiments show that the system is able to reduce the number of weed plants by 83% in the seedling area. Crop detection accuracy was measured at 97.8% (precision 0.998 and recall 0.952) with a detection time of 30 ms f(-1). This paper also shows that the crop signalling system has the advantage that prior knowledge of visual features of each crop and weed species is not required and poor visual appearance of the crop plants or weeds does not affect system performance. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.