  With rich annotation information, object detection-based automated plant
disease diagnosis systems (e.g., YOLO-based systems) often provide advantages
over classification-based systems (e.g., EfficientNet-based), such as the
ability to detect disease locations and superior classification performance.
One drawback of these detection systems is dealing with unannotated healthy
data with no real symptoms present. In practice, healthy plant data appear to
be very similar to many disease data. Thus, those models often produce
mis-detected boxes on healthy images. In addition, labeling new data for
detection models is typically time-consuming. Hard-sample mining (HSM) is a
common technique for re-training a model by using the mis-detected boxes as new
training samples. However, blindly selecting an arbitrary amount of hard-sample
for re-training will result in the degradation of diagnostic performance for
other diseases due to the high similarity between disease and healthy data. In
this paper, we propose a simple but effective training strategy called
hard-sample re-mining (HSReM), which is designed to enhance the diagnostic
performance of healthy data and simultaneously improve the performance of
disease data by strategically selecting hard-sample training images at an
appropriate level. Experiments based on two practical in-field eight-class
cucumber and ten-class tomato datasets (42.7K and 35.6K images) show that our
HSReM training strategy leads to a substantial improvement in the overall
diagnostic performance on large-scale unseen data. Specifically, the object
detection model trained using the HSReM strategy not only achieved superior
results as compared to the classification-based state-of-the-art
EfficientNetV2-Large model and the original object detection model, but also
outperformed the model using the HSM strategy in multiple evaluation metrics.
