Identification of grape diseases using image analysis and BP neural networks

Zhu, Jun; Wu, Ang; Wang, Xiushan; Zhang, Hao

doi:10.1007/s11042-018-7092-0

Cited by 70 publications

(35 citation statements)

References 21 publications

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“…e prediction model used in this paper is the BP neural network algorithm, which is a feedforward neural network for error backward update. It is often used for bank risk analysis [16], geological disaster monitoring [17], image and handwritten digit recognition [18,19], and other fields. BP neural network consists of three parts: input layer, middle layer, and output layer.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network

Chen

Zhang

2021

Complexity

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Aiming at the problem that the credit card default data of a financial institution is unbalanced, which leads to unsatisfactory prediction results, this paper proposes a prediction model based on k-means SMOTE and BP neural network. In this model, k-means SMOTE algorithm is used to change the data distribution, and then the importance of data features is calculated by using random forest, and then it is substituted into the initial weights of BP neural network for prediction. The model effectively solves the problem of sample data imbalance. At the same time, this paper constructs five common machine learning models, KNN, logistics, SVM, random forest, and tree, and compares the classification performance of these six prediction models. The experimental results show that the proposed algorithm can greatly improve the prediction performance of the model, making its AUC value from 0.765 to 0.929. Moreover, when the importance of features is taken as the initial weight of BP neural network, the accuracy of model prediction is also slightly improved. In addition, compared with the other five prediction models, the comprehensive prediction effect of BP neural network is better.

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Section: Bp Neural Networkmentioning

confidence: 99%

Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network

Chen

Zhang

2021

Complexity

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“…Subsequently, the selected feature vector was fed to a support vector machine (SVM), which achieved a 97% recognition accuracy for citrus diseases and 90.4% on a private dataset. In [22], a grape leaf disease detection method based on a back-propagation neural network was introduced. First, images denoised using a wavelet transform-based Wiener filtering technique, and the infected region was segmented using the Otsu segmentation method.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Deep Learning Framework for Fruits Diseases Classification

Thanikachalam¹,

Shanthi²,

Kalirajan³

et al. 2022

Computers, Materials &Amp; Continua

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Agriculture has been an important research area in the field of image processing for the last five years. Diseases affect the quality and quantity of fruits, thereby disrupting the economy of a country. Many computerized techniques have been introduced for detecting and recognizing fruit diseases. However, some issues remain to be addressed, such as irrelevant features and the dimensionality of feature vectors, which increase the computational time of the system. Herein, we propose an integrated deep learning framework for classifying fruit diseases. We consider seven types of fruits, i.e., apple, cherry, blueberry, grapes, peach, citrus, and strawberry. The proposed method comprises several important steps. Initially, data increase is applied, and then two different types of features are extracted. In the first feature type, texture and color features, i.e., classical features, are extracted. In the second type, deep learning characteristics are extracted using a pretrained model. The pretrained model is reused through transfer learning. Subsequently, both types of features are merged using the maximum mean value of the serial approach. Next, the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm. Finally, the selected features are classified using multiple classifiers. An evaluation is performed on the PlantVillage dataset, and an accuracy of 99% is achieved. A comparison with recent techniques indicate the superiority of the proposed method.

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“…The experimental results showed that the best performance of the two classifiers was 90% and 96.6% respectively. J. Zhu, et al [3] proposed an automatic grape leaf disease detection method using image analysis and back-propagation neural network. According to the lesion region, the authors extracted five features of it such as shape complexity, circularity, perimeter, rectangularity, and area.…”

Section: Introductionmentioning

confidence: 99%

Grape Leaf Spot Identification Under Limited Samples by Fine Grained-GAN

et al. 2021

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In practice, early detection of disease is of high importance to practical value, corresponding measures can be taken at the early stage of plant disease. However, in the early stage of disease or when a rare disease occurs, there are limited training samples in practice, which makes machine learning especially deep learning models hardly work well while the stronger representative ability needs large-scale training data. To solve this problem, a fine grained-GAN based grape leaf spot identification method was proposed for local spot area image data augmentation to the generated local spot area images which were added and fed them into deep learning models for training to further strengthen the generalization ability of the classification models, which can effectively improve the accuracy and robustness of the prediction. Including 500 early-stage grape leaf spot images every category were fed into the proposed fine grained-GAN for local spot area data augmentation, 1000 local spot area sub images every category were generated in this study. The improved faster R-CNN was integrated in fine grained-GAN as local spot area detector. After that, the segmented sub-images and generated sub-images were mixed as training data input into the deep learning models, while the original segmented sub-images were used for testing. Experimental results showed that the proposed method had achieved higher identification accuracy on five state-of-art deep learning models; especially ResNet-50 had got 96.27% accuracy, which obtained significant improvements than other data augmentation methods and verified its satisfactory performance. This is of great practical significance for rare diseases or limited training samples.INDEX TERMS Fine grained-GAN, grape leaf spot identification, deep learning, few-shot learning, agricultural engineering.

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Identification of grape diseases using image analysis and BP neural networks

Cited by 70 publications

References 21 publications

Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network

Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network

An Integrated Deep Learning Framework for Fruits Diseases Classification

Grape Leaf Spot Identification Under Limited Samples by Fine Grained-GAN

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