Detection of Bacterial Wilt on Enset Crop Using Deep Learning Approach
Detection of Bacterial Wilt on Enset Crop Using Deep Learning Approach
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DOI：
10.4028/www.scientific.net/jera.51.131
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发表时间：
2020-01-01
影响因子：
0.7
通讯作者：
Debelee, Taye Girma
中科院分区：
文献类型：
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作者：
Afework, Yidnekachew Kibru;Debelee, Taye Girma
青枯病是最主要的决定因素，因为它会导致 Enset 作物生产的粮食质量和数量严重下降。因此，早期发现青枯病对于诊断和防治该病具有重要意义。为此，提出了一种深度学习方法，可以通过使用作物的健康和受感染的叶子图像来检测疾病。特别是，卷积神经网络架构旨在将从不同农场收集的图像分类为患病或健康。在农业领域专家的帮助下，总共使用了 4896 张从农场直接拍摄的图像来训练所提出的模型。使用这些图像来训练所提出的模型，并应用数据增强技术来生成更多图像。除了训练所提出的模型之外，还使用我们的数据集训练预训练模型 VGG16。所提出的模型实现了 98.5% 的平均准确率，VGG16 预训练模型通过使用 32 的小批量大小和 0.001 的学习率实现了 96.6% 的平均准确率。初步结果表明，该方法在光照、复杂背景、不同分辨率、可变尺度、旋转和真实场景图像方向等挑战性条件下的有效性。
Bacterial Wilt disease is the most determinant factor as it results in a serious reduction in the quality and quantity of food produced by Enset crop. Therefore, early detection of Bacterial Wilt disease is important to diagnose and fight the disease. To this end, a deep learning approach that can detect the disease by using healthy and infected leave images of the crop is proposed. In particular, a convolutional neural network architecture is designed to classify the images collected from different farms as diseased or healthy. A total of 4896 images that were captured directly from the farm with the help of experts in the field of agriculture was used to train the proposed model. The proposed model was trained using these images and data augmentation techniques was applied to generate more images. Besides training the proposed model, a pre-trained model namely VGG16 is trained by using our dataset. The proposed model achieved a mean accuracy of 98.5% and the VGG16 pre-trained model achieved a mean accuracy of 96.6% by using a mini-batch size of 32 and a learning rate of 0.001. The preliminary results demonstrated that the effectiveness of the proposed approach under challenging conditions such as illumination, complex background, different resolutions, variable scale, rotation, and orientation of the real scene images.