Enhancing Anomaly Detection in Plant Disease Recognition with Knowledge Ensemble

Jiuqing Dong^1*Heng Zhou²Alvaro Fuentes²Sook Yoon³Dong Sun Park²

• ¹School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, China
• ²Department of Electronic Engineering, Jeonbuk National University, Jeonju, Republic of Korea
• ³Department of Computer Engineering, Mokpo National University, Mokpo, Republic of Korea

Plant diseases pose a significant threat to agriculture, impacting food security and public health.Most existing plant disease recognition methods operate within closed-set settings, where disease categories are fixed during training, making them ineffective against novel diseases. This study extends plant disease recognition to an open-set scenario, enabling the identification of both known and unknown classes for real-world applicability. We first benchmark the anomaly detection performance of three major visual frameworks-convolutional neural networks (CNNs), vision transformers (ViTs), and vision-language models (VLMs)-under varying fine-tuning strategies.To address the limitations of individual models, we propose a knowledge-ensemble-based method that integrates the general knowledge from pre-trained models with domain-specific knowledge from fine-tuned models in the logit and feature spaces. Our method significantly improves over existing baselines. For example, on vision-language models with 16-shot per class, our approach reduces the FPR@TPR95 from 43.88% to 7.05%; in the all-shot setting, it reduces the FPR@TPR95 from 15.38% to 0.71%. Extensive experiments confirm the robustness and generalizability of our approach across diverse model architectures and training paradigms. We will release the code soon at https://github.com/JiuqingDong/Enhancing Anomaly Detection.