  Recently, object detection methods (OD; e.g., YOLO-based models) have been
widely utilized in plant disease diagnosis. These methods demonstrate
robustness to distance variations and excel at detecting small lesions compared
to classification methods (CL; e.g., CNN models). However, there are issues
such as low diagnostic performance for hard-to-detect diseases and high
labeling costs. Additionally, since healthy cases cannot be explicitly trained,
there is a risk of false positives. We propose the Hierarchical object
detection and recognition framework (HODRF), a sophisticated and highly
integrated two-stage system that combines the strengths of both OD and CL for
plant disease diagnosis. In the first stage, HODRF uses OD to identify regions
of interest (ROIs) without specifying the disease. In the second stage, CL
diagnoses diseases surrounding the ROIs. HODRF offers several advantages: (1)
Since OD detects only one type of ROI, HODRF can detect diseases with limited
training images by leveraging its ability to identify other lesions. (2) While
OD over-detects healthy cases, HODRF significantly reduces these errors by
using CL in the second stage. (3) CL's accuracy improves in HODRF as it
identifies diagnostic targets given as ROIs, making it less vulnerable to size
changes. (4) HODRF benefits from CL's lower annotation costs, allowing it to
learn from a larger number of images. We implemented HODRF using YOLOv7 for OD
and EfficientNetV2 for CL and evaluated its performance on a large-scale
dataset (4 crops, 20 diseased and healthy classes, 281K images). HODRF
outperformed YOLOv7 alone by 5.8 to 21.5 points on healthy data and 0.6 to 7.5
points on macro F1 scores, and it improved macro F1 by 1.1 to 7.2 points over
EfficientNetV2.
