  Cauliflower is a hand-harvested crop that must fulfill high-quality standards
in sales making the timing of harvest important. However, accurately
determining harvest-readiness can be challenging due to the cauliflower head
being covered by its canopy. While deep learning enables automated
harvest-readiness estimation, errors can occur due to field-variability and
limited training data. In this paper, we analyze the reliability of a
harvest-readiness classifier with interpretable machine learning. By
identifying clusters of saliency maps, we derive reliability scores for each
classification result using knowledge about the domain and the image
properties. For unseen data, the reliability can be used to (i) inform farmers
to improve their decision-making and (ii) increase the model prediction
accuracy. Using RGB images of single cauliflower plants at different
developmental stages from the GrowliFlower dataset, we investigate various
saliency mapping approaches and find that they result in different quality of
reliability scores. With the most suitable interpretation tool, we adjust the
classification result and achieve a 15.72% improvement of the overall accuracy
to 88.14% and a 15.44% improvement of the average class accuracy to 88.52% for
the GrowliFlower dataset.
