Deep neural networks with transfer learning in millet crop images

Coulibaly, Solemane; Kamsu-Foguem, Bernard; Kamissoko, Dantouma; Traoré, Daouda

doi:10.1016/j.compind.2019.02.003

Cited by 223 publications

(91 citation statements)

References 15 publications

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“…In addition, the improvement of the classification accuracy can also contribute to change detection. Deep learning methods have recently progressed considerably and have played a key role in addressing remote sensing image classification [65,66]. The validity of classification using Convolutional Neural Network (CNN) in South China has been verified in our previous studies [67][68][69].…”

Section: Discussionmentioning

confidence: 79%

Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Liu

et al. 2020

Remote Sensing

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Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an ‘anytime’ forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability.

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Section: Discussionmentioning

confidence: 79%

Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Liu

et al. 2020

Remote Sensing

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“…As a typical feed-forward neural network enlightened by the cerebral cortical neuron of the animal, CNN adopts a common way of supervised learning with primary hierarchical structure [65], [66]. The frequent overfitting issue in DL-based approaches can be effectively addressed via CNN [67]. CNN is composed of different layers, including data input layer, convolution layer, ReLU activation layer, pooling layer and fully connected layer.…”

Section: B Architecture and Principle Of Cnnmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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“…Therefore, considerable time is saved when training the entire network is not required [22,23]. There are two approaches which can be used in transfer learning: feature extraction and fine-tuning [19,24]. Feature extraction refers to the values extracted from pre-trained models, and they are called deep features.…”

Section: Transfer Learning and Pre-trained Modelsmentioning

confidence: 99%

Transfer Learning-Based Search Model for Hot Pepper Diseases and Pests

Yin

Park

et al. 2020

Agriculture

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The use of conventional classification techniques to recognize diseases and pests can lead to an incorrect judgment on whether crops are diseased or not. Additionally, hot pepper diseases, such as “anthracnose” and “bacterial spot” can be erroneously judged, leading to incorrect disease recognition. To address these issues, multi-recognition methods, such as Google Cloud Vision, suggest multiple disease candidates and allow the user to make the final decision. Similarity-based image search techniques, along with multi-recognition, can also be used for this purpose. Content-based image retrieval techniques have been used in several conventional similarity-based image searches, using descriptors to extract features such as the image color and edge. In this study, we use eight pre-trained deep learning models (VGG16, VGG19, Resnet 50, etc.) to extract the deep features from images. We conducted experiments using 28,011 image data of 34 types of hot pepper diseases and pests. The search results for diseases and pests were similar to query images with deep features using the k-nearest neighbor method. In top-1 to top-5, when using the deep features based on the Resnet 50 model, we achieved recognition accuracies of approximately 88.38–93.88% for diseases and approximately 95.38–98.42% for pests. When using the deep features extracted from the VGG16 and VGG19 models, we recorded the second and third highest performances, respectively. In the top-10 results, when using the deep features extracted from the Resnet 50 model, we achieved accuracies of 85.6 and 93.62% for diseases and pests, respectively. As a result of performance comparison between the proposed method and the simple convolutional neural network (CNN) model, the proposed method recorded 8.62% higher accuracy in diseases and 14.86% higher in pests than the CNN classification model.

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Deep neural networks with transfer learning in millet crop images

Cited by 223 publications

References 15 publications

Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Transfer Learning-Based Search Model for Hot Pepper Diseases and Pests

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