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.

翻译：花椰菜是一种需要手工采收的作物，其销售必须满足高质量标准，因此采收时机的把握至关重要。然而，由于花椰菜花球常被叶冠覆盖，准确判断其采收成熟度颇具挑战性。尽管深度学习能够实现自动化采收成熟度评估，但田间变异性与训练数据有限可能导致预测错误。本文利用可解释机器学习分析采收成熟度分类器的可靠性。通过识别显著性图聚类，我们结合领域知识与图像属性，为每个分类结果推导出可靠性评分。对于未见数据，该可靠性评分可用于：(i) 帮助种植户优化决策；(ii) 提升模型预测精度。基于GrowliFlower数据集中不同发育阶段单株花椰菜的RGB图像，我们研究了多种显著性图方法，发现不同方法产生的可靠性评分质量存在差异。采用最优解释工具调整分类结果后，在GrowliFlower数据集上，整体准确率提升15.72%至88.14%，平均类别准确率提升15.44%至88.52%。