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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks
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Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks

机译：使用UAV图像和深卷积神经网络自动识别大豆叶疾病

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Plant diseases are a crucial issue in agriculture. An accurate and automatic identification of leaf diseases could help to develop an early response to reduce economic losses. Recent research in plant diseases has adopted deep neural networks. However, such research has used the models as a black-box passing the labeled images through the networks. This letter presents an analysis of the network weights for the automatic recognition of soybean leaf diseases applied to images taken straight from a small and cheap unmanned aerial vehicle (UAV). To achieve high accuracy, we evaluated four deep neural network models trained with different parameters for fine-tuning (FT) and transfer learning. Data augmentation and dropout were used during the network training to avoid overfitting. Our methodology consists of using the SLIC method to segment the plant leaves in the top-view images obtained during the flight. We tested our data set created from real flight inspections in an end-to-end computer vision approach. Results strongly suggest that the FT of parameters substantially improves the identification accuracy.
机译：植物疾病是农业至关重要的问题。准确和自动鉴定叶片疾病可能有助于发展早期反应，以降低经济损失。最近的植物疾病研究采用了深度神经网络。然而，这种研究使用模型作为通过网络传递标记图像的黑匣子。这封信提出了对自动识别大豆叶疾病的网络权重的分析，该叶片应用于直接从小型和廉价的无人机（UAV）直接拍摄的图像。为了实现高精度，我们评估了具有不同参数的四种深度神经网络模型，用于微调（FT）和转移学习。在网络培训期间使用数据增强和丢失，以避免过度装备。我们的方法包括使用SLIC方法在飞行期间获得的顶视图中分割工厂叶子。我们测试了在端到端计算机视觉方法中从真实飞行检查创建的数据集。结果强烈建议参数的FT大大提高了识别准确性。

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