文献类型：期刊文献

英文题名：Enhancing Plant Recognition in Intelligent Agricultural Irrigation Systems Using Stable Diffusion and Lora Models with CFloss

作者：Lu, Yingzhu[1]; Tang, Chengyu[2]; Fan, Yawen[3]; Zhou, Qin[4]

机构：[1] Nanjing University of Posts and Telecommunications, Department of Computer Science and Technology, Nanjing, China; [2] Nanjing Forestry University, Department of Computer Science and Technology, Nanjing, China; [3] School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China; [4] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2024

外文期刊名：Proceedings - 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

收录：EI(收录号：20251318133219)

语种：英文

外文关键词：Subirrigation

摘要：This paper proposes a novel approach to optimize the plant recognition function of intelligent agricultural irrigation system, utilizing Stable Diffusion model and Lora (Low-Rank Adaptation) model. Stable Diffusion model is a latent diffusion model that converts text into images, while Lora is a method of fine-tuning large models by reducing trainable parameters while maintaining model performance as much as possible. By combining the Stable Diffusion model and Lora model, this paper trains custom models for each plant species using a small amount of data and generates corresponding images of each variety to assist training, thus optimizing classification results. In addition, to address the issue of imbalanced data, this study also introduces Focal Loss to mitigate the impact of both class imbalance and difficulty in classification. Building upon Focal Loss, CFloss is created as the model's loss function. This method was tested on the OxfordFlower102 dataset used by the original model, and the results demonstrate that it effectively improves classification accuracy and significantly enhances the plant recognition function of the irrigation system. ? 2024 IEEE.