Efficient Aphid Counting Network Based on Knowledge Distillation

作者：Fengna Cheng, Haoyu Geng, Xue Liu, Juntao Wei, Xuesong Jiang, Hongping Zhou, Jiafu Yang · 发表于：Applied Engineering in Agriculture · 年份：2025 · DOI：10.13031/aea.16203 · 研究领域：Smart Agriculture and AI、Advanced Neural Network Applications、Fire Detection and Safety Systems

Highlights Lightweight network for accurate aphid counting. Two complementary feature measurements for efficient knowledge transfer. Achieving comparable counting performance using about one-fifth of the parameters of the baseline. Strong applicability to other tasks, such as mealworm counting. Abstract. In the fields of agriculture, forestry, and horticulture, aphids are a pernicious pest with the most deleterious effect on crops, the widest geographical range, and the most rapid reproductive rate. Precisely handling these pests is therefore an urgent need in planting automation, and a vital prerequisite is to count these aphids. The aphid counting model based on computer vision accomplishes precise aphid counting by learning a mapping between images and real labels. Nevertheless, in the practical application deployment, prevailing high-performance network models usually consist of a large number of parameters and have high hardware requirements, making it problematic to apply to edge devices. To tackle these challenges, this work studied a robust feature transfer strategy for efficient distillation of high-performance aphid-counting networks. Specifically, two complementary loss functions were explored to extract effective knowledge from the teacher network and enhance the learning capability of the student network. Experimental results showed that, compared with recent methods, our method achieved significantly better results across multiple datasets with minimal computati...