Author:
Liu Bo,Wei Shusen,Zhang Fan,Guo Nawei,Fan Hongyu,Yao Wei
Abstract
IntroductionTomato leaf diseases can cause major yield and quality losses. Computer vision techniques for automated disease recognition show promise but face challenges like symptom variations, limited labeled data, and model complexity.MethodsPrior works explored hand-crafted and deep learning features for tomato disease classification and multi-task severity prediction, but did not sufficiently exploit the shared and unique knowledge between these tasks. We present a novel multi-task distillation learning (MTDL) framework for comprehensive diagnosis of tomato leaf diseases. It employs knowledge disentanglement, mutual learning, and knowledge integration through a multi-stage strategy to leverage the complementary nature of classification and severity prediction.ResultsExperiments show our framework improves performance while reducing model complexity. The MTDL-optimized EfficientNet outperforms single-task ResNet101 in classification accuracy by 0.68% and severity estimation by 1.52%, using only 9.46% of its parameters.DiscussionThe findings demonstrate the practical potential of our framework for intelligent agriculture applications.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献