arXiv 2507.093752025-07-12cs.CV

基于卷积神经网络的多类作物病理学自动分类：面向实时精准农业的深度学习方法

Automated Multi-Class Crop Pathology Classification via Convolutional Neural Networks: A Deep Learning Approach for Real-Time Precision Agriculture

University of California, San Diego（加州大学圣地亚哥分校）；University of Pennsylvania（宾夕法尼亚大学）

Sourish Suri, Yifei Shao

备注：: 29 pages, 10 figures, 1 table. Code available at: https://github.com/Sourish85/CNN-CROP-DIS-DETECTOR

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AI总结：

本研究提出一种基于CNN的图像分类系统，利用TensorFlow和Keras构建深度学习流程自动检测八种作物病害，集成治疗推荐模块并部署于移动平台，为精准农业提供可扩展的实时诊断工具。

摘要：

作物病害是农业生产力和全球粮食安全的重大障碍，在大型农场中尤其如此，因为早期识别往往被延误或不准确。本研究引入了一个基于卷积神经网络（CNN）的图像分类系统，旨在利用叶片图像自动检测和分类八种常见作物病害。该方法包含完整的深度学习流程：从大型标注数据集中获取图像，通过调整大小、归一化和数据增强进行预处理，并使用TensorFlow和Keras的Sequential API进行模型训练。CNN架构包含三个具有递增滤波器尺寸和ReLU激活的卷积层，随后是最大池化、展平和全连接层，最后以softmax输出进行多类分类。该系统实现了高训练准确率（约90%），并在未见数据上表现出可靠性能，尽管约60%的验证准确率表明存在轻微过拟合。值得注意的是，该模型集成了治疗推荐模块，通过将每种检测到的病害映射到合适的杀虫剂或杀菌剂干预措施来提供可操作的指导。此外，该解决方案部署在开源、移动兼容的平台上，使偏远地区的农民能够进行实时图像诊断。本研究为精准农业领域贡献了一个可扩展且易于获取的工具，减少了对人工检查的依赖，促进了可持续的病害管理实践。通过将深度学习与实用的农学支持相结合，这项工作突显了CNN在变革作物健康监测和提升全球粮食生产韧性方面的潜力。

英文摘要：

Crop diseases present a significant barrier to agricultural productivity and global food security, especially in large-scale farming where early identification is often delayed or inaccurate. This research introduces a Convolutional Neural Network (CNN)-based image classification system designed to automate the detection and classification of eight common crop diseases using leaf imagery. The methodology involves a complete deep learning pipeline: image acquisition from a large, labeled dataset, preprocessing via resizing, normalization, and augmentation, and model training using TensorFlow with Keras' Sequential API. The CNN architecture comprises three convolutional layers with increasing filter sizes and ReLU activations, followed by max pooling, flattening, and fully connected layers, concluding with a softmax output for multi-class classification. The system achieves high training accuracy (~90%) and demonstrates reliable performance on unseen data, although a validation accuracy of ~60% suggests minor overfitting. Notably, the model integrates a treatment recommendation module, providing actionable guidance by mapping each detected disease to suitable pesticide or fungicide interventions. Furthermore, the solution is deployed on an open-source, mobile-compatible platform, enabling real-time image-based diagnostics for farmers in remote areas. This research contributes a scalable and accessible tool to the field of precision agriculture, reducing reliance on manual inspection and promoting sustainable disease management practices. By merging deep learning with practical agronomic support, this work underscores the potential of CNNs to transform crop health monitoring and enhance food production resilience on a global scale.

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