MTDL-EPDCLD: A Multi-Task Deep-Learning-Based System for Enhanced Precision Detection and Diagnosis of Corn Leaf Diseases.
MTDL-EPDCLD: A Multi-Task Deep-Learning-Based System for Enhanced Precision Detection and Diagnosis of Corn Leaf Diseases.
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DOI：
10.3390/plants12132433
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发表时间：
2023-06-23
期刊：
影响因子：
--
通讯作者：
Che H
中科院分区：
文献类型：
--
作者：
Dai D;Xia P;Zhu Z;Che H
玉米叶部病害造成农业生产的重大损失，对全球粮食安全构成挑战。准确、及时的检测和诊断是实施有效控制措施的关键。为了提高玉米叶部病害的检测和诊断能力，提出了一种基于多任务深度学习的玉米叶部病害检测与诊断系统(MTDL-EPDCLD)，并利用跨平台的软件开发框架Qt框架开发了一个移动应用程序。该系统包括快速准确的健康状态识别(RAHSI)任务1和细粒度关注疾病分类(FDCA)任务2。针对任务1建立了一个具有空间注意机制的浅层CNN-4模型，对健康和病叶的识别准确率达到98.73%。对于任务2，设计了定制的MobileNetV3大注意力模型。与其他主流深度学习模型相比，该模型的正确率达到94.44%，准确率、召回率和F1分数都提高了4-8%。此外，该模型的曲线下面积(AUC)为0.9993，与其他主流模型相比，表现出0.002-0.007的提高。MTDL-EPDCLD系统为玉米叶部病害的检测和诊断提供了准确和高效的工具，支持关于疾病管理、提高作物产量和改善粮食安全的明智决策。这项研究为玉米叶部病害的检测和诊断提供了一个很有前途的解决方案，其持续的发展和实施可能会对农业实践和结果产生重大影响。
Corn leaf diseases lead to significant losses in agricultural production, posing challenges to global food security. Accurate and timely detection and diagnosis are crucial for implementing effective control measures. In this research, a multi-task deep learning-based system for enhanced precision detection and diagnosis of corn leaf diseases (MTDL-EPDCLD) is proposed to enhance the detection and diagnosis of corn leaf diseases, along with the development of a mobile application utilizing the Qt framework, which is a cross-platform software development framework. The system comprises Task 1 for rapid and accurate health status identification (RAHSI) and Task 2 for fine-grained disease classification with attention (FDCA). A shallow CNN-4 model with a spatial attention mechanism is developed for Task 1, achieving 98.73% accuracy in identifying healthy and diseased corn leaves. For Task 2, a customized MobileNetV3Large-Attention model is designed. It achieves a val_accuracy of 94.44%, and improvements of 4–8% in precision, recall, and F1 score from other mainstream deep learning models. Moreover, the model attains an area under the curve (AUC) of 0.9993, exhibiting an enhancement of 0.002–0.007 compared to other mainstream models. The MTDL-EPDCLD system provides an accurate and efficient tool for corn leaf disease detection and diagnosis, supporting informed decisions on disease management, increased crop yields, and improved food security. This research offers a promising solution for detecting and diagnosing corn leaf diseases, and its continued development and implementation may substantially impact agricultural practices and outcomes.
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