用深度卷积网络自动识别南瓜叶病，准确率达86%
Automated Disease Diagnosis in Pumpkin Plants Using Advanced CNN Models
- 用ResNet、DenseNet、EfficientNet等模型分析2000张南瓜叶图像
- DenseNet-121综合表现最优，整体准确率86%，兼顾效率
- 适合农业科研人员和智慧农业开发者参考
南瓜是全球重要作物，其产量对粮食安全至关重要，尤其在发展地区。及时准确地检测南瓜叶片疾病对减少产量与品质损失极为关键。传统依赖人工判断的方法主观性强，易错失干预时机。本文利用公开的2000张高分辨率南瓜叶图像数据集，评估了ResNet、DenseNet、EfficientNet等先进卷积神经网络（CNN）模型在识别五类叶片状态（健康、霜霉病、白粉病、花叶病、细菌性叶斑病）中的表现。通过微调预训练模型并优化超参数，发现ResNet-34、DenseNet-121和EfficientNet-B7为表现优异的模型，其中DenseNet-121在准确率与计算复杂度间取得最佳平衡，整体准确率达到86%。研究证明了CNN在南瓜病害自动化诊断中的潜力，有助于提升农业生产力，减少经济损失。
原文摘要 · Abstract (English)
Pumpkin is a vital crop cultivated globally, and its productivity is crucial for food security, especially in developing regions. Accurate and timely detection of pumpkin leaf diseases is essential to mitigate significant losses in yield and quality. Traditional methods of disease identification rely heavily on subjective judgment by farmers or experts, which can lead to inefficiencies and missed opportunities for intervention. Recent advancements in machine learning and deep learning offer promising solutions for automating and improving the accuracy of plant disease detection. This paper presents a comprehensive analysis of state-of-the-art Convolutional Neural Network (CNN) models for classifying diseases in pumpkin plant leaves. Using a publicly available dataset of 2000 highresolution images, we evaluate the performance of several CNN architectures, including ResNet, DenseNet, and EfficientNet, in recognizing five classes: healthy leaves and four common diseases downy mildew, powdery mildew, mosaic disease, and bacterial leaf spot. We fine-tuned these pretrained models and conducted hyperparameter optimization experiments. ResNet-34, DenseNet-121, and EfficientNet-B7 were identified as top-performing models, each excelling in different classes of leaf diseases. Our analysis revealed DenseNet-121 as the optimal model when considering both accuracy and computational complexity achieving an overall accuracy of 86%. This study underscores the potential of CNNs in automating disease diagnosis for pumpkin plants, offering valuable insights that can contribute to enhancing agricultural productivity and minimizing economic losses.
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