用深度学习提升作物叶片病害检测准确率，提出高效CNN模型。
Leaf diseases detection using deep learning methods
- 设计新型CNN架构，优化超参数与训练方法。
- 对比多种模型，新模型在多个数据集上表现更优。
- 适合农业智能监测、植物保护领域研究人员参考。
本研究旨在开发一种新的深度学习方法，用于基于叶片图像数据集的作物病害识别与检测。我们分析了现有方法面临的挑战，并探讨了深度学习如何克服这些障碍以提高检测精度。为此，提出了一种新型病害检测方法，包含高效的网络架构及其超参数配置和优化策略。通过比较不同架构的性能，确定了最佳配置，构建了能快速检测病害的有效模型。除了对预训练模型的研究外，还提出了一种基于CNN的新模型，可高效识别和检测植物病害。此外，评估了所提模型的有效性，并与若干先进的预训练架构进行了结果对比。
原文摘要 · Abstract (English)
This study, our main topic is to devlop a new deep-learning approachs for plant leaf disease identification and detection using leaf image datasets. We also discussed the challenges facing current methods of leaf disease detection and how deep learning may be used to overcome these challenges and enhance the accuracy of disease detection. Therefore, we have proposed a novel method for the detection of various leaf diseases in crops, along with the identification and description of an efficient network architecture that encompasses hyperparameters and optimization methods. The effectiveness of different architectures was compared and evaluated to see the best architecture configuration and to create an effective model that can quickly detect leaf disease. In addition to the work done on pre-trained models, we proposed a new model based on CNN, which provides an efficient method for identifying and detecting plant leaf disease. Furthermore, we evaluated the efficacy of our model and compared the results to those of some pre-trained state-of-the-art architectures.
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