  This paper investigates the problem of class-incremental object detection for
agricultural applications where a model needs to learn new plant species and
diseases incrementally without forgetting the previously learned ones. We adapt
two public datasets to include new categories over time, simulating a more
realistic and dynamic scenario. We then compare three class-incremental
learning methods that leverage different forms of knowledge distillation to
mitigate catastrophic forgetting. Our experiments show that all three methods
suffer from catastrophic forgetting, but the Dynamic Y-KD approach, which
additionally uses a dynamic architecture that grows new branches to learn new
tasks, outperforms ILOD and Faster-ILOD in most settings both on new and old
classes.
  These results highlight the challenges and opportunities of continual object
detection for agricultural applications. In particular, we hypothesize that the
large intra-class and small inter-class variability that is typical of plant
images exacerbate the difficulty of learning new categories without interfering
with previous knowledge. We publicly release our code to encourage future work.
