A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition.
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition.
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DOI：
10.3390/s17092022
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发表时间：
2017-09-04
期刊：
影响因子：
--
通讯作者：
Park DS
中科院分区：
文献类型：
--
作者：
Fuentes A;Yoon S;Kim SC;Park DS
植物病虫害是农业部门面临的主要挑战。准确和快速地检测植物中的疾病和害虫有助于开发早期处理技术，同时大大减少经济损失。深度神经网络的最新发展使研究人员能够大幅提高目标检测和识别系统的准确性。在本文中，我们提出了一种基于深度学习的方法，使用各种分辨率的相机设备现场捕获的图像来检测番茄植物中的疾病和害虫。我们的目标是找到更适合我们任务的深度学习架构。因此，我们考虑三个主要的检测器系列：更快的基于区域的卷积神经网络（Faster R-CNN），基于区域的全卷积网络（R-FCN）和单次多盒检测器（SSD），出于本工作的目的，它们被称为“深度学习元架构”。我们将这些元架构中的每一个与“深度特征提取器”相结合，例如VGG网络和残差网络（ResNet）。我们展示了深层元架构和特征提取器的性能，并提出了一种用于局部和全局类注释和数据增强的方法，以提高准确性并减少训练过程中的误报数量。我们在大型番茄病虫害数据集上对系统进行端到端的训练和测试，该数据集包含具有挑战性的病虫害图像，包括几个类间和类外变化，例如感染状态和植物位置。实验结果表明，我们提出的系统可以有效地识别九种不同类型的疾病和害虫，有能力处理复杂的情况下，从植物的周围地区。
Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called “deep learning meta-architectures”. We combine each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant’s surrounding area.
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影响因子：
1.2
作者：
Chaerani, Reni;Voorrips, Roeland E.
通讯作者：
Voorrips, Roeland E.
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V
影响因子：
3.1
作者：
Gutierrez-Aguirre, Ion;Mehle, Natasa;Ravnikar, Maja
通讯作者：
Ravnikar, Maja
影响因子：
2.7
作者：
Canizares, M. C.;Rosas-Diaz, T.;Moriones, E.
通讯作者：
Moriones, E.