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Alternaria blotch, Brown spot, Mosaic, Grey spot, and Rust are 5 common apple leaf diseases that severely impact apple production and quality. At present, although many CNN methods have been proposed for apple leaf diseases, there are still lack of apple leaf disease detection models that can be applied on mobile devices, which limits their application in practical production. This paper proposes a light-weight CNN model that can be deployed on mobile devices to detect apple leaf diseases in real time. First, a dataset of apple leaf diseases composed of simple background images and complex background images, which is called AppleDisease5, is constructed via data augmentation technology and data annotation technology. Then a basic module called MEAN block(Mobile End AppleNet block) is proposed to increase the detection speed and reduce model’s size by reconstructing the common 3×3 convolution. Meanwhile, the Apple-Inception module is built by introducing GoogLeNet’s Inception module and replacing all 3×3 convolution kernels with MEAN block in Inception. Finally, a new apple leaf disease detection model, MEAN-SSD(Mobile End AppleNet based SSD algorithm), is built by using the MEAN block and Apple-Inception module. The experiment results show that MEAN-SSD can achieve the detection performance of 83.12% mAP and a speed of 12.53 FPS, which illustrates that the novel MEAN-SSD model can efficiently and accurately detect 5 common apple leaf diseases on mobile devices.
Henan Sun; Haowei Xu; Bin Liu; Dongjian He; Jinrong He; Haixi Zhang; Nan Geng. MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks. Computers and Electronics in Agriculture 2021, 189, 106379 .
AMA StyleHenan Sun, Haowei Xu, Bin Liu, Dongjian He, Jinrong He, Haixi Zhang, Nan Geng. MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks. Computers and Electronics in Agriculture. 2021; 189 ():106379.
Chicago/Turabian StyleHenan Sun; Haowei Xu; Bin Liu; Dongjian He; Jinrong He; Haixi Zhang; Nan Geng. 2021. "MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks." Computers and Electronics in Agriculture 189, no. : 106379.
Anthracnose, brown spot, mites, black rot, downy mildew, and leaf blight are six common grape leaf pests and diseases, which cause severe economic losses to the grape industry. Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry. This paper proposes a novel recognition approach that is based on improved convolutional neural networks for the diagnoses of grape leaf diseases. First, based on 4,023 images collected in the field and 3,646 images collected from public data sets, a data set of 107,366 grape leaf images is generated via image enhancement techniques. Afterward, Inception structure is applied for strengthening the performance of multi-dimensional feature extraction. In addition, a dense connectivity strategy is introduced to encourage feature reuse and strengthen feature propagation. Ultimately, a novel CNN-based model, namely, DICNN, is built and trained from scratch. It realizes an overall accuracy of 97.22% under the hold-out test set. Compared to GoogLeNet and ResNet-34, the recognition accuracy increases by 2.97% and 2.55%, respectively. The experimental results demonstrate that the proposed model can efficiently recognize grape leaf diseases. Meanwhile, this study explores a new approach for the rapid and accurate diagnosis of plant diseases that establishes a theoretical foundation for the application of deep learning in the field of agricultural information.
Bin Liu; Zefeng Ding; Liangliang Tian; Dongjian He; Shuqin Li; Hongyan Wang. Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks. Frontiers in Plant Science 2020, 11, 1082 .
AMA StyleBin Liu, Zefeng Ding, Liangliang Tian, Dongjian He, Shuqin Li, Hongyan Wang. Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks. Frontiers in Plant Science. 2020; 11 ():1082.
Chicago/Turabian StyleBin Liu; Zefeng Ding; Liangliang Tian; Dongjian He; Shuqin Li; Hongyan Wang. 2020. "Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks." Frontiers in Plant Science 11, no. : 1082.
Black rot, Black measles, Leaf blight and Mites of grape are four common grape leaf diseases that seriously affect grape yield. However, the existing research lacks a real-time detecting method for grape leaf diseases, which cannot guarantee the healthy growth of grape plants. In this article, a real-time detector for grape leaf diseases based on improved deep convolutional neural networks is proposed. This article first expands the grape leaf disease images through digital image processing technology, constructing the grape leaf disease dataset (GLDD). Based on GLDD and the Faster R-CNN detection algorithm, a deep-learning-based Faster DR-IACNN model with higher feature extraction capability is presented for detecting grape leaf diseases by introducing the Inception-v1 module, Inception-ResNet-v2 module and SE-blocks. The experimental results show that the detection model Faster DR-IACNN achieves a precision of 81.1% mAP on GLDD, and the detection speed reaches 15.01 FPS. This research indicates that the real-time detector Faster DR-IACNN based on deep learning provides a feasible solution for the diagnosis of grape leaf diseases and provides guidance for the detection of other plant diseases.
Xiaoyue Xie; Yuan Ma; Bin Liu; Jinrong He; Shuqin Li; Hongyan Wang. A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks. Frontiers in Plant Science 2020, 11, 1 .
AMA StyleXiaoyue Xie, Yuan Ma, Bin Liu, Jinrong He, Shuqin Li, Hongyan Wang. A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks. Frontiers in Plant Science. 2020; 11 ():1.
Chicago/Turabian StyleXiaoyue Xie; Yuan Ma; Bin Liu; Jinrong He; Shuqin Li; Hongyan Wang. 2020. "A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks." Frontiers in Plant Science 11, no. : 1.
The identification of grape leaf diseases based on deep learning is critical to controlling the spread of diseases and ensuring the healthy development of the grape industry. Focusing on the lack of training images of grape leaf diseases, this paper proposes a novel model named Leaf GAN, which is based on generative adversarial networks (GANs), to generate images of four different grape leaf diseases for training identification models. A generator model with degressive channels is first designed to generate grape leaf disease images; then, the dense connectivity strategy and instance normalization are fused into an efficient discriminator to identify real and fake disease images by utilizing their excellent feature extraction capability on grape leaf lesions. Finally, the deep regret gradient penalty method is applied to stabilize the training process of the model. Using a total of 4,062 grape leaf disease images, the Leaf GAN model ultimately generates 8,124 grape leaf disease images. The generated grape leaf disease images based on Leaf GAN model can obtain better performance than DCGAN and WGAN in terms of the Fréchet inception distance. The experimental results show that the proposed Leaf GAN model generates sufficient grape leaf disease images with prominent lesions, providing a feasible solution for the data augmentation of grape leaf disease images. For the eight prevailing classification models with the expanded dataset, the identification performance based on CNNs indicated higher accuracies, whereby all the accuracies were better than those of the initial dataset with other data augmentation methods. Among them, Xception achieves a recognition accuracy of 98.70% on the testing set. The results demonstrate that the proposed data augmentation method represents a new approach to overcoming the overfitting problem in disease identification and can effectively improve the identification accuracy.
Bin Liu; Cheng Tan; Shuqin Li; Jinrong He; Hongyan Wang. A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification. IEEE Access 2020, 8, 102188 -102198.
AMA StyleBin Liu, Cheng Tan, Shuqin Li, Jinrong He, Hongyan Wang. A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification. IEEE Access. 2020; 8 (99):102188-102198.
Chicago/Turabian StyleBin Liu; Cheng Tan; Shuqin Li; Jinrong He; Hongyan Wang. 2020. "A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification." IEEE Access 8, no. 99: 102188-102198.
Retinex image enhancement algorithm occupies an important position in eliminating image uneven exposure, low contrast, and smog influence. However, with the increasing of image resolution, the real-time performance of the serial Retinex algorithm has not satisfied the requirements of practical applications. This paper proposes an OpenMP-based parallel Retinex algorithm. The parallelism of the Retinex algorithm is first identified by theoretical analyses. Then, the time-consuming sub-algorithms such as Gaussian convolution and exponential transformation, of the serial algorithm are designed and executed in parallel. On Tianhe-2 supercomputer platform, the experimental results show that the speedup of the parallel algorithm is significantly improved, and the test image set achieves an average speedup of 12. It indicates that the parallel algorithm can satisfy the needs of real-time processing in image enhancement field.
Shixiong Cheng; Bin Liu; Dongjian He; Jinrong He; Yuancheng Li; Yanning Du. A Parallel Retinex Image Enhancement Algorithm Based on OpenMP. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 376 -381.
AMA StyleShixiong Cheng, Bin Liu, Dongjian He, Jinrong He, Yuancheng Li, Yanning Du. A Parallel Retinex Image Enhancement Algorithm Based on OpenMP. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():376-381.
Chicago/Turabian StyleShixiong Cheng; Bin Liu; Dongjian He; Jinrong He; Yuancheng Li; Yanning Du. 2019. "A Parallel Retinex Image Enhancement Algorithm Based on OpenMP." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 376-381.
With the development of image processing technology, pencil drawing has been widely used in video games and mobile phone applications. However, the existing pencil drawing algorithms require a large amount of time to convert a real picture into a pencil drawing; hence, it is difficult to apply them to real-time systems. This paper proposes a parallel fast pencil drawing generation algorithm based on graphics processing unit (GPU) to accelerate the real-time rendering process of sketch painting. The parallelism of the pencil drawing generation algorithm is identified via theoretical analysis at first. Then, sub-algorithms of the sequential algorithm are designed in parallel using the compute unified device architecture (CUDA) programming model and executed via thread-level parallel techniques. Furthermore, an optimal cache pattern of data that reduces the access time of the most frequently used data is structured using shared memory and constant memory. Finally, task-level parallelism is achieved by CUDA stream technology, which overlaps independent sub-tasks for further acceleration. On the CUDA platform, the experimental results demonstrate that the proposed parallel algorithm can achieve a significant increase in speedup. The proposed algorithm achieves a performance improvement of 448.59 times compared to the sequential algorithm, on 2560W1920-resolution images, and maintain a high degree of similarity with the real pencil paintings. Hence, the proposed algorithm is suitable for real-time pencil drawing rendering and has promising application prospects in non-photorealistic rendering.
Jiyan Qiu; Bin Liu; Jinrong He; Chaoyang Liu; Yuancheng Li. Parallel Fast Pencil Drawing Generation Algorithm Based on GPU. IEEE Access 2019, 7, 83543 -83555.
AMA StyleJiyan Qiu, Bin Liu, Jinrong He, Chaoyang Liu, Yuancheng Li. Parallel Fast Pencil Drawing Generation Algorithm Based on GPU. IEEE Access. 2019; 7 (99):83543-83555.
Chicago/Turabian StyleJiyan Qiu; Bin Liu; Jinrong He; Chaoyang Liu; Yuancheng Li. 2019. "Parallel Fast Pencil Drawing Generation Algorithm Based on GPU." IEEE Access 7, no. 99: 83543-83555.
Alternaria leaf spot, Brown spot, Mosaic, Grey spot and Rust are five common types of apple leaf diseases that severely affect apple yield. However, the existing research lacks an accurate and fast detector of apple diseases for ensuring the healthy development of the apple industry. This paper proposes a deep learning approach that is based on improved convolutional neural networks for the real-time detection of apple leaf diseases. In this study, the apple leaf disease dataset (ALDD), which is composed of laboratory images and complex images under real field conditions, is first constructed via data augmentation and image annotation technologies. Based on this, a new apple leaf disease detection model that uses deep convolutional neural networks is proposed by introducing the GoogLeNet Inception structure and Rainbow concatenation. Finally, under the hold-out testing dataset, using a dataset of 26,377 images of diseased apple leaves, the proposed INAR-SSD (SSD with Inception module and Rainbow concatenation) model is trained to detect these five common apple leaf diseases. The experimental results show that the INAR-SSD model realizes a detection performance of 78.80.
Peng Jiang; Yuehan Chen; Bin Liu; Dongjian He; Chunquan Liang. Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks. IEEE Access 2019, 7, 59069 -59080.
AMA StylePeng Jiang, Yuehan Chen, Bin Liu, Dongjian He, Chunquan Liang. Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks. IEEE Access. 2019; 7 (99):59069-59080.
Chicago/Turabian StylePeng Jiang; Yuehan Chen; Bin Liu; Dongjian He; Chunquan Liang. 2019. "Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks." IEEE Access 7, no. 99: 59069-59080.
With the explosive growth of image big data in the agriculture field, image segmentation algorithms are confronted with unprecedented challenges. As one of the most important image segmentation technologies, the fuzzy C-means (FCM) algorithm has been widely used in the field of agricultural image segmentation as it provides simple computation and high quality segmentation. However, due to its large amount of computation, the sequential FCM algorithm is too slow to finish the segmentation task within an acceptable time. This paper proposes a parallel FCM segmentation algorithm based on the distributed memory computing platform Apache Spark for agricultural image big data. The input image is first converted from the RGB color space to the Lab color space and generates point cloud data. Then, point cloud data are partitioned and stored in different computing nodes, in which the membership degrees of pixel points to different cluster centers are calculated, and the cluster centers are updated iteratively in a data-parallel form until the stopping condition is satisfied. Finally, point cloud data are restored after clustering for reconstructing the segmented image. On the Spark platform, the performance of the parallel fuzzy C-means algorithm is evaluated and reaches an average speedup of 12.54 on 10 computing nodes. Experimental results show that the Spark-based parallel fuzzy C-means algorithm can obtain a significant increase in speedup, and the agricultural image testing set delivers a better performance improvement of 128% than the Hadoop-based approach. This research indicates that the Spark-based parallel FCM algorithm provides a faster speed of segmentation for agricultural image big data and has better scaleup and sizeup rates.
Bin Liu; Songrui He; Dongjian He; Yin Zhang; Mohsen Guizani. A Spark-Based Parallel Fuzzy $c$ -Means Segmentation Algorithm for Agricultural Image Big Data. IEEE Access 2019, 7, 42169 -42180.
AMA StyleBin Liu, Songrui He, Dongjian He, Yin Zhang, Mohsen Guizani. A Spark-Based Parallel Fuzzy $c$ -Means Segmentation Algorithm for Agricultural Image Big Data. IEEE Access. 2019; 7 (99):42169-42180.
Chicago/Turabian StyleBin Liu; Songrui He; Dongjian He; Yin Zhang; Mohsen Guizani. 2019. "A Spark-Based Parallel Fuzzy $c$ -Means Segmentation Algorithm for Agricultural Image Big Data." IEEE Access 7, no. 99: 42169-42180.
Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.
Bin Liu; Yun Zhang; Dongjian He; Yuxiang Li. Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks. Symmetry 2017, 10, 11 .
AMA StyleBin Liu, Yun Zhang, Dongjian He, Yuxiang Li. Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks. Symmetry. 2017; 10 (1):11.
Chicago/Turabian StyleBin Liu; Yun Zhang; Dongjian He; Yuxiang Li. 2017. "Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks." Symmetry 10, no. 1: 11.
Speculative multithreading (SpMT) is a thread-level automatic parallelization technique that can accelerate sequential programs, especially for irregular applications that are hard to be parallelized by conventional approaches. Thread partition plays a critical role in SpMT. Conventional machine learning-based thread partition approaches applied machine learning to offline guide partition, but could not explicitly explore the law between partition and performance. In this paper, we build a parametric model (Qinling) with a multiple regression method to discover the inherent law between thread partition and performance. The paper firstly extracts unpredictable parameters that determine the performance of thread partition in SpMT; secondly, we build a parametric model Qinling with extracted parameters and speedups, and train Qinling offline, as well as apply it to predict the theoretical speedups of unseen applications. Finally, validation is done. Prophet, which consists of an automatic parallelization compiler and a multi-core simulator, is used to obtain real speedups of the input programs. Olden and SPEC2000 benchmarks are used to train and validate the parametric model. Experiments show that Qinling delivers a good performance to predict speedups of unseen programs, and provides feedback guidance for Prophet to obtain the optimal partition parameters.
Yuxiang Li; Yinliang Zhao; Bin Liu. Qinling: A Parametric Model in Speculative Multithreading. Symmetry 2017, 9, 180 .
AMA StyleYuxiang Li, Yinliang Zhao, Bin Liu. Qinling: A Parametric Model in Speculative Multithreading. Symmetry. 2017; 9 (9):180.
Chicago/Turabian StyleYuxiang Li; Yinliang Zhao; Bin Liu. 2017. "Qinling: A Parametric Model in Speculative Multithreading." Symmetry 9, no. 9: 180.
Emotion-aware computing can recognize, interpret, process, and simulate human affects. These programs in this area are compute-intensive applications, so they need to be executed in parallel. Loops usually have regular structures and programs spend significant amounts of time executing them, and thus loops are ideal candidates for exploiting the parallelism of sequential programs. However, it is difficult to decide which set of loops should be parallelized to improve program performance. The existing research is one-size-fits-all strategy and cannot guarantee to select profitable loops to be parallelized. This paper proposes a novel loop selection approach based on machine learning (ML-based) for selecting the profitable loops and paralleling them on multi-core by speculative multithreading (SpMT). It includes establishing sufficient training examples, building and applying prediction model to select profitable loops for speculative parallelization. Using the ML-based loop selection approach, an unseen emotion-aware sequential program can obtain a stable, much higher speedup than the one-size-fits-all approach. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, the novel loop selection approach is evaluated and reaches an average speedup of 1.87 on a 4-core processor. Experiment results show that the ML-based approach can obtain a significant increase in speedup, and Olden benchmarks deliver a better performance improvement of 6.70% on a 4-core than the one-size-fits-all approach.
Bin Liu; Jinrong He; Yaojun Geng; Lvwen Huang; Shuqin Li. Toward Emotion-Aware Computing: A Loop Selection Approach Based on Machine Learning for Speculative Multithreading. IEEE Access 2017, 5, 3675 -3686.
AMA StyleBin Liu, Jinrong He, Yaojun Geng, Lvwen Huang, Shuqin Li. Toward Emotion-Aware Computing: A Loop Selection Approach Based on Machine Learning for Speculative Multithreading. IEEE Access. 2017; 5 ():3675-3686.
Chicago/Turabian StyleBin Liu; Jinrong He; Yaojun Geng; Lvwen Huang; Shuqin Li. 2017. "Toward Emotion-Aware Computing: A Loop Selection Approach Based on Machine Learning for Speculative Multithreading." IEEE Access 5, no. : 3675-3686.