Plant identification based on very deep convolutional neural networks

Zhu, Heyan; Liu, Qinglin; Qi, Yuankai; Huang, Xinyuan; Jiang, Feng; Zhang, Shengping

doi:10.1007/s11042-017-5578-9

Cited by 40 publications

(18 citation statements)

References 45 publications

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“…Transfer learning [41][42][43] is a common approach to handle lack of training data in a dataset. It is widely believed that networks trained on the ImageNet dataset [44] are able to learn general features from it; then, this network can be fine-tuned on other datasets for a specific task such as face recognition [45,46], classification [47][48][49], detection [50,51], and visual tracking [52][53][54]. Therefore, it makes transfer learning an essential approach, especially for the small datasets.…”

Section: Transfer Learningmentioning

confidence: 99%

Deep person re-identification in UAV images

Grigorev

Tian²,

Rho

et al. 2019

EURASIP J. Adv. Signal Process.

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The person re-identification is one of the most significant problems in computer vision and surveillance systems. The recent success of deep convolutional neural networks in image classification has inspired researchers to investigate the application of deep learning to the person re-identification. However, the huge amount of research on this problem considers classical settings, where pedestrians are captured by static surveillance cameras, although there is a growing demand for analyzing images and videos taken by drones. In this paper, we aim at filling this gap and provide insights on the person re-identification from drones. To our knowledge, it is the first attempt to tackle this problem under such constraints. We present the person re-identification dataset, named DRone HIT (DRHIT01), which is collected by using a drone. It contains 101 unique pedestrians, which are annotated with their identities. Each pedestrian has about 500 images. We propose to use a combination of triplet and large-margin Gaussian mixture (L-GM) loss to tackle the drone-based person re-identification problem. The proposed network equipped with multi-branch design, channel group learning, and combination of loss functions is evaluated on the DRHIT01 dataset. Besides, transfer learning from the most popular person re-identification datasets is evaluated. Experiment results demonstrate the importance of transfer learning and show that the proposed model outperforms the classic deep learning approach.

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Section: Transfer Learningmentioning

confidence: 99%

Deep person re-identification in UAV images

Grigorev

Tian²,

Rho

et al. 2019

EURASIP J. Adv. Signal Process.

Self Cite

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“…On the other hand, spectroscopy methods need to build complex models, but models are not universal. Spectral technology has high requirements for instruments and equipment, and it is prone to giving false‐positive results . In general, spectroscopy detection has the advantages of high accuracy, fast response, and intuitive observation results, which can quickly and safely detect crop diseases.…”

Section: Introductionmentioning

confidence: 99%

A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

Yang

Wang

et al. 2020

J Sci Food Agric

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BACKGROUND Rice blast fungus is a worldwide disease, and it is one of the most serious rice diseases in the north and south rice fields in China. The initial symptoms of rice blast are not obvious, and the speed of transmission is fast. Manual identification is time‐consuming and laborious. At present, it is a great challenge to realize rapid and accurate early identification of rice blast. RESULTS In this paper, an identification method based on crop disease spores' diffraction fingerprint texture for rice blast was studied; this method utilizes the light field and texture features of diffraction images. To verify the reliability of the model that we proposed, we selected two methods of manual identification and machine recognition to compare and detect rice blast spores. The experimental results show that the identification of light diffraction characteristics is not only higher than the traditional manual recognition by microscope (increased by more than 0.3%), but also faster after neural network training (increased by more than 90%). The diffraction recognition method used in this study, based on crop disease spores' diffraction fingerprint texture, can be completed in a few seconds, and its test accuracy is 97.18%. CONCLUSION The proposed method, a rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture, has certain advantages compared with the existing manual identification by microscope. This method can be applied to the recognition of rice blast in agricultural research. © 2020 Society of Chemical Industry

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“…Deep learning method has the robustness and the ability of generalization so that it outperforms in many fields such as: signal processing [4], pedestrian detection [5], face recognition [6], road crack detection [7], and biomedical image analysis [8]. Deep learning techniques have also accomplished impressive outcome in the agriculture field and were benefit for horticultural workers and smallholders including: recognition of weeds [9] selection of fine seeds [10], pest identification [11], fruit counting [12], and research on land cover [13]. The wide spread of deep CNNs in the agriculture field has lead to a big progress especially in plant diseases classification, in which they can find high variance of pathological symptoms in visual appearance.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Efficient Pre-trained Networks based on Transfer Learning for Tomato Leaf Diseases Classification

Gharghory¹

2020

IJACSA

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Early diagnosis and accurate identification to tomato leaf diseases contribute on controlling the diffusion of infection and guarantee healthy to the plant which in role result in increasing the crop harvest. Nine common types of tomato leaf diseases have a great effect on the quality and quantity of tomato crop yield. The tradition approaches of features extraction and image classification cannot ensure a high accuracy rate for leaf diseases identification. This paper suggests an automatic detection approach for tomato leaf diseases based on the fine tuning and transfer learning to the pre-trained of deep Convolutional Neural Networks. Three pre-trained deep networks based on transfer learning: AlexNet, VGG-16 Net and SqueezeNet are suggested for their performances analysis in tomato leaf diseases classification. The proposed networks are carried out on two different dataset, one of them is a small dataset using only four different diseases while the other is a large dataset of leaves accompanied with symptoms of nine diseases and healthy leaves. The performance of the suggested networks is evaluated in terms of classification accuracy and the elapsed time during their training. The performance of the suggested networks using the small dataset are also compared with that of the-state-of-the-art technique in literature. The experimental results with the small dataset demonstrate that the accuracy of classification of the suggested networks outperform by 8.1% and 15% over the classification accuracy of the technique in literature. On other side when using the large dataset, the proposed pre-trained AlexNet achieves high classification accuracy by 97.4% and the consuming time during its training is lower than those of the other pre-trained networks. Generally, it can be concluded that AlexNet has outstanding performance for diagnosing the tomato leaf diseases in terms of accuracy and execution time compared to the other networks. On contrary, the performance of VGG-16 Net in metric of classification accuracy is the best yet the largest consuming time among other networks.

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Plant identification based on very deep convolutional neural networks

Cited by 40 publications

References 45 publications

Deep person re-identification in UAV images

Deep person re-identification in UAV images

A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

Performance Analysis of Efficient Pre-trained Networks based on Transfer Learning for Tomato Leaf Diseases Classification

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