Plant classification system for crop /weed discrimination without segmentation

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Finely-grained annotated datasets for image-based plant phenotyping (2016, background): “A similar problem in the context of precision agriculture is weed vs. crop classiﬁcation, whereupon the interest is to separate a valid crop from a weed in images, usually acquired by automated robotic mechanisms (Haug et al., 2014).”
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021, methodology): “For example, the discrimination between crop and weed in a carrot farm was performed by an autonomous system through the implementation of the Random Forest classifier (Haug et al., 2014).”
Agricultural Robotics: The Future of Robotic Agriculture (2018, background): “. Vision systems are also required for detection, segmentation, classification and tracking of objects such as fruits, plants, livestock, people, etc., and semantic segmentation of crops versus weeds [32, 33, 34], etc. to enable scene analysis (understanding “what” is “where” and “when”) and safe op”
Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs (2017, methodology): “Haug et al. [7] propose a method to distinguish carrot plants and weeds in RGB and near infra-red (NIR) images.”