Genetic Programming with Aggregate Channel Features for Flower Localization Using Limited Training Data

Flower localization is a crucial image pre-processing step for subsequent classification/recognition that confronts challenges with diverse flower species, varying imaging conditions, and limited data. Existing flower localization methods face limitations, including reliance on color information, low model interpretability, and a large demand for training data. This paper proposes a new genetic programming (GP) approach called ACFGP with a novel representation to automated flower localization with limited training data. The novel GP representation enables ACFGP to evolve effective programs for generating aggregate channel features and achieving flower localization in diverse scenarios. Comparative evaluations against the baseline benchmark algorithm and YOLOv8 demonstrate ACFGP's superior performance. Further analysis highlights the effectiveness of the aggregate channel features generated by ACFGP programs, demonstrating the superiority of ACFGP in addressing challenging flower localization tasks.