Li Yang, Chao Xuewei
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Front Plant Sci. 2021 Dec 24;12:811241. doi: 10.3389/fpls.2021.811241. eCollection 2021.
The crop pest recognition based on the convolutional neural networks is meaningful and important for the development of intelligent plant protection. However, the current main implementation method is deep learning, which relies heavily on large amounts of data. As known, current big data-driven deep learning is a non-sustainable learning mode with the high cost of data collection, high cost of high-end hardware, and high consumption of power resources. Thus, toward sustainability, we should seriously consider the trade-off between data quality and quantity. In this study, we proposed an embedding range judgment (ERJ) method in the feature space and carried out many comparative experiments. The results showed that, in some recognition tasks, the selected good data with less quantity can reach the same performance with all training data. Furthermore, the limited good data can beat a lot of bad data, and their contrasts are remarkable. Overall, this study lays a foundation for data information analysis in smart agriculture, inspires the subsequent works in the related areas of pattern recognition, and calls for the community to pay more attention to the essential issue of data quality and quantity.
基于卷积神经网络的农作物害虫识别对于智能植物保护的发展具有重要意义。然而，当前的主要实现方法是深度学习，它严重依赖大量数据。众所周知，当前大数据驱动的深度学习是一种不可持续的学习模式，数据收集成本高、高端硬件成本高且电力资源消耗大。因此，为了实现可持续性，我们应该认真考虑数据质量和数量之间的权衡。在本研究中，我们在特征空间中提出了一种嵌入范围判断（ERJ）方法，并进行了许多对比实验。结果表明，在一些识别任务中，选择数量较少的优质数据可以达到与所有训练数据相同的性能。此外，有限的优质数据可以胜过大量的劣质数据，它们之间的对比非常显著。总体而言，本研究为智慧农业中的数据信息分析奠定了基础，启发了模式识别相关领域的后续工作，并呼吁业界更多地关注数据质量和数量这一关键问题。
