| Abstrakt: |
As robotic systems become increasingly integrated into plant phenotyping processes, the quality of images captured plays an increasingly crucial role in accurate data collection and analysis. Here we address the challenge of assessing and enhancing image quality in the context of plant phenotyping robot using a pan-tilt-zoom camera. We present a data-driven approach using machine learning, specifically the Random Forest classifier, to classify both blurred and sharp images. Our method involves feature extraction, data preprocessing, hyperparameter tuning, and cross-validation. The resulting model demonstrates promising performance as indicated by its accuracy, precision, recall, area under the curve (AUC), and feature importance analysis. Notably, our results support a highly accurate classifier, achieving a correct classification rate of 95% for sharp images and 92% for blurred ones, a receiver operating characteristic curve with an AUC of 0.93, and a precision-recall curve with an average precision of 0.91. Shapley Additive Explanations analysis identifies "edge density" and "mean gradient magnitude" as influential to the classifier, offering valuable insights for future feature engineering and model refinement. The classifier has a short inference time (2.8 s) on a Raspberry Pi 4B computer, both improving the quality of captured images and automatically eliminating blurred images. By enhancing image quality assessment, this research improves data reliability and the overall effectiveness of plant phenotyping robots. We discuss the implications of these findings and their practical relevance and suggest directions for future research. [ABSTRACT FROM AUTHOR] |
