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

Tian, Yunong; Yang, Guodong; Wang, Zhe; Li, En; Liang, Zize

doi:10.1155/2019/7630926

Cited by 144 publications

(84 citation statements)

References 27 publications

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“…The CycleGAN [102] is essentially two mirror-symmetrical GAN, it can learn the characteristics of a class of data and generate similar data. The research showed that compared with traditional image enhancement methods, the CycleGAN method greatly enriched the diversity of training data sets [103].…”

Section: Analysis Of the Surveyed DL Applicationsmentioning

confidence: 99%

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

134

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Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.

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Section: Analysis Of the Surveyed DL Applicationsmentioning

confidence: 99%

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

134

View full text Add to dashboard Cite

show abstract

“…Machine learning, for an instance, plays a key role in detecting such pests and epidemics. In the past decades, a considerable volume of studies with different machine learning algorithm have been executed for Plant disease detection under different environmental conditions, in different countries, and for different plants such as tomato [7], potato [8], rice [9], cassava [10], mango [11], apple [12,13] , general plants [14,15], and Olive [16,17], etc. Jagan Mohan et al [4] presented a system that firstly used SIFT to extract featured from the paddy plant; secondly the AdaBoost classifier was used for disease detection with identification rate 83.33%.…”

Section: Related Workmentioning

confidence: 99%

“…Singh, et al [11] presented a multilayer convolutional neural network (MCNN) based approach for identifying Anthracnose fungal disease affect Mango leaves, the system get average accuracy of 97.13%. Detection of anthracnose lesion in apple fruit using adapted DenseNet model was presented in [12], and it achieved an overall accuracy of 95.57% for disease identification. Apple leaf diseases using deep-CNNs is proposed in [13], in this system; GoogLeNet Inception structure and Rainbow concatenation (VGG-INCEP model.)…”

Section: Related Workmentioning

confidence: 99%

An Efficient Deep Learning Model for Olive Diseases Detection

Alruwaili¹,

Alanazi²,

El-Ghany³

et al. 2019

IJACSA

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Worldwide, plant diseases adversely influence both the quality and quantity of crop production. Thus, the early detection of such diseases proves efficient in enhancing the crop quality and reducing the production loss. However, the detection of plant diseases either via the farmers' naked eyes or their traditional tools or even within laboratories is still an error prone and time consuming process. The current paper presents a Deep Learning (DL) model with a view to developing an efficient detector of olive diseases. The proposed model is distinguishable from others in a number of novelties. It utilizes an efficient parameterized transfer learning model, a smart data augmentation with balanced number of images in every category, and it functions in more complex environments with enlarged and enhanced dataset. In contrast to the lately developed state-ofart methods, the results show that our proposed method achieves higher measurements in terms of accuracy, precision, recall, and F 1-Measure.

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“…The problem of fruit detection is also widely analyzed in the literature, especially during the detection of fruits in orchards [19] and damage detection [20]. The YOLO V3 model [18], the Faster R-CNN model [21], and their modifications are the state-of-the-art fruit detection approaches [19,20]. The use of object detection and recognition techniques for multi-class fruit classification was presented in [22].…”

Section: Related Workmentioning

confidence: 99%

A Vision-Based Method Utilizing Deep Convolutional Neural Networks for Fruit Variety Classification in Uncertainty Conditions of Retail Sales

Rudnik

Michalski

2019

Applied Sciences

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This study proposes a double-track method for the classification of fruit varieties for application in retail sales. The method uses two nine-layer Convolutional Neural Networks (CNNs) with the same architecture, but different weight matrices. The first network classifies fruits according to images of fruits with a background, and the second network classifies based on images with the ROI (Region Of Interest, a single fruit). The results are aggregated with the proposed values of weights (importance). Consequently, the method returns the predicted class membership with the Certainty Factor (CF). The use of the certainty factor associated with prediction results from the original images and cropped ROIs is the main contribution of this paper. It has been shown that CFs indicate the correctness of the classification result and represent a more reliable measure compared to the probabilities on the CNN outputs. The method is tested with a dataset containing images of six apple varieties. The overall image classification accuracy for this testing dataset is excellent (99.78%). In conclusion, the proposed method is highly successful at recognizing unambiguous, ambiguous, and uncertain classifications, and it can be used in a vision-based sales systems in uncertain conditions and unplanned situations.

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

Cited by 144 publications

References 27 publications

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

An Efficient Deep Learning Model for Olive Diseases Detection

A Vision-Based Method Utilizing Deep Convolutional Neural Networks for Fruit Variety Classification in Uncertainty Conditions of Retail Sales

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