Weeds have seriously impacted crop planting and caused serious human losses. The intelligent weed identification algorithm based on the convolutional neural network has made some achievements in intelligent agriculture. However, the current results rely on a large number of labeled weed data, which is costly to obtain. How to effectively identify without big data is a key task in the current field. We designed a high-information data-centric weed images data identification system based on a triple filter. The system consists of three modules: nearest-neighbor core metric, unobserved components model, and outlier detection. Extensive scientific experiments have shown that our system requires only a small amount of data to achieve excellent performance in weed identification tasks. Our system saves 5% to 20% of the data. When using only 80% of the data, our system achieves the model performance obtained with 100% of data training. Compared with other methods, this method improves the accuracy by 4.9% and reaches a state-of-the-art performance. Our work solves the problem of relying on image data in smart agriculture, provides a scheme for weed identification tasks, and provides valuable ideas for future intelligent agricultural research.