  Panicle density of cereal crops such as wheat and sorghum is one of the main
components for plant breeders and agronomists in understanding the yield of
their crops. To phenotype the panicle density effectively, researchers agree
there is a significant need for computer vision-based object detection
techniques. Especially in recent times, research in deep learning-based object
detection shows promising results in various agricultural studies. However,
training such systems usually requires a lot of bounding-box labeled data.
Since crops vary by both environmental and genetic conditions, acquisition of
huge amount of labeled image datasets for each crop is expensive and
time-consuming. Thus, to catalyze the widespread usage of automatic object
detection for crop phenotyping, a cost-effective method to develop such
automated systems is essential. We propose a point supervision based active
learning approach for panicle detection in cereal crops. In our approach, the
model constantly interacts with a human annotator by iteratively querying the
labels for only the most informative images, as opposed to all images in a
dataset. Our query method is specifically designed for cereal crops which
usually tend to have panicles with low variance in appearance. Our method
reduces labeling costs by intelligently leveraging low-cost weak labels (object
centers) for picking the most informative images for which strong labels
(bounding boxes) are required. We show promising results on two publicly
available cereal crop datasets - Sorghum and Wheat. On Sorghum, 6 variants of
our proposed method outperform the best baseline method with more than 55%
savings in labeling time. Similarly, on Wheat, 3 variants of our proposed
methods outperform the best baseline method with more than 50% of savings in
labeling time.
