Human agricultural activities are always accompanied by pests and diseases, which have brought great losses to the production of crops. Intelligent algorithms based on deep learning have achieved some achievements in the field of pest control, but relying on a large amount of data to drive consumes a lot of resources, which is not conducive to the sustainable development of smart agriculture. The research in this paper starts with data, and is committed to finding efficient data, solving the data dilemma, and helping sustainable agricultural development. Starting from the data, this paper proposed an Edge Distance-Entropy data evaluation method, which can be used to obtain efficient crop pests, and the data consumption is reduced by 5% to 15% compared with the existing methods. The experimental results demonstrate that this method can obtain efficient crop pest data, and only use about 60% of the data to achieve 100% effect. Compared with other data evaluation methods, the method proposed in this paper achieve state-of-the-art results. The work conducted in this paper solves the dilemma of the existing intelligent algorithms for pest control relying on a large amount of data, and has important practical significance for realizing the sustainable development of modern smart agriculture.

The usual active learning is to sample unlabeled set by designing efficient sample information evaluation algorithms. However, information redundancy between candidate sets is often overlooked. This can cause similar data to be labeled repeatedly, producing ineffective gains for the model. In this paper, we proposed an Unsupervised Redundant Feature Elimination Active Learning module (URFEAL), which utilizes the information feature coincidence of the unlabeled set to eliminate information redundant data, thus guaranteeing the validity of each candidate data. URFEAL consists of feature clusterer and eliminator. The feature clusterer computes class boundaries based on feature densities to discretize each class of the candidate set, and the eliminator judges data similarity by overlapping degree to eliminate redundant data features. Furthermore, we propose an anti-noise sampling strategy Outlier Feature Elimination (OFE) in URFEAL to filter mislabeled sets for relabeling in the data sampling stage. We extensively evaluate our method by image classification and perform experimental validation on CIFAR-10, CIFAR-100 and CALTECH-101. The experimental results show that the improvements we make are especially significant for most existing active learning algorithms in the low data stage, which demonstrates the effectiveness and generality of URFEAL.