Toward Sustainability: Trade-Off Between Data Quality and Quantity in Crop Pest Recognition - Li, Yang; Chao, Xuewei (Frontiers in Plant Science, 2021)
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 high cost of data collection, high cost of high-end hardware and high consumption of power resources. Thus, towards sustainability, we should seriously consider the trade-off between data quality and quantity. In this paper, we proposed an ERJ feature analysis method and carried out many comparative experiments. The results showed that there really exist some combinations of good data with less quantity, the small amount of good selected data can reach the same testing performance based on all the data. Furthermore, the limited good data can beat a lot of bad data, and their contrasts are remarkable. Overall, this work lays a foundation for data information analysis in the smart agriculture, inspires the subsequent works in related areas of pattern recognition, and calls for the community to pay more attention to the essential issue of data quality and quantity.