Distance-Entropy: An Effective Indicator for Selecting Informative Data - Li, Yang; Chao, Xuewei (Frontiers in Plant Science, 2022)
Smart agriculture is inseparable from data gathering, analysis, and utilization. High-quality data improves the efficiency of intelligent algorithms and helps reduce the costs of data collection and transmission. However, the current image quality assessment research focuses on visual quality, ignoring the crucial information aspect. In this work, taking the crop pest recognition task as an example, we proposed an effective indicator of distance-entropy to distinguish the good and bad data from the perspective of information. Many comparative experiments considering mapping feature dimensions and base data size were conducted to testify the validity and robustness. Both the numerical and visual results demonstrate the effectiveness and stability of the proposed distance-entropy method. In general, this study is a relatively cutting-edge work in smart agriculture, which calls for attention to the data information quality assessment and provides some inspiration for the subsequent research on data mining and dataset optimization for practical applications.