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Tomato leaf disease recognition based on multi-task distillation learning
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DOI：10.3389/fpls.2023.1330527.png)
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摘要
En 中文
Introduction Tomato 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.Methods Prior 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.Results Experiments 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.Discussion The findings demonstrate the practical potential of our framework for intelligent agriculture applications.
Keyword:
multi-task learning
knowledge distillation
tomato leaf diseases
disease classification
severity prediction
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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.8
论文数:
3.5W
被引数:
14.7W
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