轻量模型AlexNet在手机端实现87%番茄病害识别准确率
A Novel Feature Extraction Model for the Detection of Plant Disease from Leaf Images in Low Computational Devices
- 融合多种深度学习技术提取叶面病害特征
- 在1万张图像上测试，AlexNet达87%准确率且轻量快速
- 适合手机等低算力设备，农民可即时诊断病害
植物病害对农业生产构成重大威胁。早期精准检测可减少作物损失和农药使用。传统识别方法耗时且需专业技能。若农民能直接拍摄叶片照片快速检测，将极大提升效率并及时采取措施。本研究提出一种新型特征提取方法，用于在低成本计算设备（如手机）上检测番茄病害。该方法结合多种深度学习技术，从叶片图像中提取鲁棒且具有区分性的特征。在五种先进模型（AlexNet、ResNet50、VGG16、VGG19、MobileNet）上进行对比实验，数据集包含10,000张来自十类番茄病害及一类健康叶片的图像。实验结果表明，AlexNet在保证87%准确率的同时，具备快速轻量优势，适用于嵌入式系统及其他低处理能力设备。
原文摘要 · Abstract (English)
Diseases in plants cause significant danger to productive and secure agriculture. Plant diseases can be detected early and accurately, reducing crop losses and pesticide use. Traditional methods of plant disease identification, on the other hand, are generally time-consuming and require professional expertise. It would be beneficial to the farmers if they could detect the disease quickly by taking images of the leaf directly. This will be a time-saving process and they can take remedial actions immediately. To achieve this a novel feature extraction approach for detecting tomato plant illnesses from leaf photos using low-cost computing systems such as mobile phones is proposed in this study. The proposed approach integrates various types of Deep Learning techniques to extract robust and discriminative features from leaf images. After the proposed feature extraction comparisons have been made on five cutting-edge deep learning models: AlexNet, ResNet50, VGG16, VGG19, and MobileNet. The dataset contains 10,000 leaf photos from ten classes of tomato illnesses and one class of healthy leaves. Experimental findings demonstrate that AlexNet has an accuracy score of 87%, with the benefit of being quick and lightweight, making it appropriate for use on embedded systems and other low-processing devices like smartphones.
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