Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods

Mohamed Farag, Jana Kierdorf, Ribana Roscher; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 651-659

Quantifying the uncertainty of machine learning models is a promising way to make better-informed decisions in digital agriculture. Efforts have been made to address this, ranging from understanding and segregating sources of uncertainty to utilizing diverse approaches for quantifying the cumulative amount. However, in order to fully realize the potential of uncertainty quantification in digital agriculture, more research is needed to compare and contrast different methods and determine which are most effective in different contexts. In this paper, we investigate inductive conformal prediction as another family of machine learning methods besides the commonly used softmax outputs and Monte Carlo dropout. Inductive conformal prediction constructs valid prediction sets by selecting a pre-defined level of predictive confidence in the system. In our experiments, we analyze this method for an image-based harvest-readiness classification task of cauliflower plants, and compare the results to softmax outputs and uncertainties derived from Monte Carlo dropout. Inductive conformal prediction turns out as a complementary tool offering distinct advantages and providing another level of information for decision support.