The modernization of smart farming is a way to improve agricultural production efficiency, and improve the agricultural production environment. Although many large models have achieved high accuracy in the task of object recognition and segmentation, they cannot really be put into use in the farming industry due to their own poor interpretability and limitations in computational volume. In this paper, we built AnYue Shelduck Dateset, which contains a total of 1951 Shelduck datasets, and performed target detection and segmentation annotation with the help of professional annotators. Based on AnYue ShelduckDateset, this paper describes DuckProcessing, an efficient and powerful module for duck identification based on real shelduckfarms. First of all, using the YOLOv8 module designed to divide the mahjong between them, Precision reached 98.10%, Recall reached 96.53% and F1 score reached 0.95 on the test set. Again using the DuckSegmentation segmentation model, DuckSegmentation reached 96.43% mIoU. Finally, the excellent DuckSegmentation was used as the teacher model, and through knowledge distillation, Deeplabv3 r50 was used as the student model, and the final student model achieved 94.49% mIoU on the test set. The method provides a new way of thinking in practical sisal duck smart farming.

翻译：智慧养殖现代化是提高农业生产效率、改善农业生产环境的重要途径。尽管许多大型模型在目标识别与分割任务中取得了较高精度，但由于其自身可解释性差、计算量受限等问题，难以真正应用于养殖行业。本文构建了包含1951份麻鸭数据的安岳麻鸭数据集，并借助专业标注人员进行了目标检测与分割标注。基于安岳麻鸭数据集，本文提出了适用于真实麻鸭养殖场景的高效强健鸭只识别模块DuckProcessing。首先采用YOLOv8模块对鸭群进行麻将分割，在测试集上精确率达到98.10%，召回率达到96.53%，F1分数达到0.95。继而使用DuckSegmentation分割模型，其mIoU达到96.43%。最后以性能优异的DuckSegmentation作为教师模型，通过知识蒸馏技术，以Deeplabv3 r50作为学生模型，最终学生模型在测试集上取得了94.49%的mIoU。该方法为实际麻鸭智慧养殖提供了新的技术思路。