Intelligent alerting for fruit-melon lesion image based on momentum deep learning

Tan, Wenxue; Zhao, Chunjiang; Wu, Haiyuan

doi:10.1007/s11042-015-2940-7

Cited by 67 publications

(26 citation statements)

References 27 publications

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“…The proposed method could reach 97.5% classification accuracy for mangosteen defect surface detection. Tan et al (2016) aimed at realizing artificial intelligence (AI)based alerting system for pests and diseases of apple. CNN was applied for recognition of apple skin lesion image collected via an infrared video sensor network.…”

Section: Quality Detection Of Fruitsmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

388

166

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Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

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Section: Quality Detection Of Fruitsmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

388

166

View full text Add to dashboard Cite

show abstract

“…A Cross Information Gain Deep Forward Neural Network was used to perform the classification, resulting in an overall accuracy of 95%. Tan et al [2015] uses synthetic infrared images of diseased and healthy melons to train a CNN, which is allowed to extract features automatically, resulting in an accuracy of up to 97.5% when classifying as healthy or not.…”

Section: Related Workmentioning

confidence: 99%

Classification of Apple Tree Disorders Using Convolutional Neural Networks

Nachtigall

Araújo

Nachtigall

2016

2016 IEEE 28th International Conference on Tools With Artificial Intelligence (ICTAI)

106

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This paper studies the use of Convolutional Neural Networks to automatically detect and classify diseases, nutritional deficiencies and damage by herbicides on apple trees from images of their leaves. This task is fundamental to guarantee a high quality of the resulting yields and is currently largely performed by experts in the field, which can severely limit scale and add to costs. By using a novel data set containing labeled examples consisting of 2539 images from 6 known disorders, we show that trained Convolutional Neural Networks are able to match or outperform experts in this task, achieving a 97.3% accuracy on a hold-out set.

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“…Deep learning achieves desirable performance in computer vision since it takes the advantage of mass amount of data and does not need to extract the image feature manually. In agriculture, deep learning techniques [11] have also been widely applied in crop detection [12][13][14] and classification [15], pest and disease identification [16] and diagnosis [17], and so on. In the classification of plant diseases, Tan et al used convolutional neural networks (CNN) to identify and diagnose the surface lesions of fruits [17].…”

Section: Introductionmentioning

confidence: 99%

Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense

et al. 2019

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Plant disease is one of the primary causes of crop yield reduction. With the development of computer vision and deep learning technology, autonomous detection of plant surface lesion images collected by optical sensors has become an important research direction for timely crop disease diagnosis. In this paper, an anthracnose lesion detection method based on deep learning is proposed. Firstly, for the problem of insufficient image data caused by the random occurrence of apple diseases, in addition to traditional image augmentation techniques, Cycle-Consistent Adversarial Network (CycleGAN) deep learning model is used in this paper to accomplish data augmentation. These methods effectively enrich the diversity of training data and provide a solid foundation for training the detection model. In this paper, on the basis of image data augmentation, densely connected neural network (DenseNet) is utilized to optimize feature layers of the YOLO-V3 model which have lower resolution. DenseNet greatly improves the utilization of features in the neural network and enhances the detection result of the YOLO-V3 model. It is verified in experiments that the improved model exceeds Faster R-CNN with VGG16 NET, the original YOLO-V3 model, and other three state-of-the-art networks in detection performance, and it can realize real-time detection. The proposed method can be well applied to the detection of anthracnose lesions on apple surfaces in orchards.

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Intelligent alerting for fruit-melon lesion image based on momentum deep learning

Cited by 67 publications

References 27 publications

Application of Deep Learning in Food: A Review

Application of Deep Learning in Food: A Review

Classification of Apple Tree Disorders Using Convolutional Neural Networks

Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense

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