Multi-growth stage plant recognition: a case study of Palmer amaranth
  (Amaranthus palmeri) in cotton (Gossypium hirsutum)
Guy RY Coleman Matthew Kutugata Michael J Walsh Muthukumar Bagavathiannan
Many advanced, image-based precision agricultural technologies for plant
breeding, field crop research, and site-specific crop management hinge on the
reliable detection and phenotyping of plants across highly variable
morphological growth stages. Convolutional neural networks (CNNs) have shown
promise for image-based plant phenotyping and weed recognition, but their
ability to recognize growth stages, often with stark differences in appearance,
is uncertain. Amaranthus palmeri (Palmer amaranth) is a particularly
challenging weed plant in cotton (Gossypium hirsutum) production, exhibiting
highly variable plant morphology both across growth stages over a growing
season, as well as between plants at a given growth stage due to high genetic
diversity. In this paper, we investigate eight-class growth stage recognition
of A. palmeri in cotton as a challenging model for You Only Look Once (YOLO)
architectures. We compare 26 different architecture variants from YOLO v3, v5,
v6, v6 3.0, v7, and v8 on an eight-class growth stage dataset of A. palmeri.
The highest mAP@[0.5:0.95] for recognition of all growth stage classes was
47.34% achieved by v8-X, with inter-class confusion across visually similar
growth stages. With all growth stages grouped as a single class, performance
increased, with a maximum mean average precision (mAP@[0.5:0.95]) of 67.05%
achieved by v7-Original. Single class recall of up to 81.42% was achieved by
v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps
(CAM) were used to understand model attention on the complex dataset. Fewer
classes, grouped by visual or size features improved performance over the
ground-truth eight-class dataset. Successful growth stage detection highlights
the substantial opportunity for improving plant phenotyping and weed
recognition technologies with open-source object detection architectures.