Rice Disease Image Recognition Based on Improved Multi-scale Stack Autoencoder

Meng, Jun; Lv, Xingchen; Fu, Lifang; Wu, Qiufeng

doi:10.5539/jas.v13n1p18

Cited by 5 publications

(6 citation statements)

References 2 publications

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“…This shows that the MSSAE model has outstanding recognition performance for real crop disease image recognition. 2 But in the image processing stage, he extracts disease features in different color spaces, and then performs image segmentation and other operations on the disease. This may cause the processed image to be too different from the original image.…”

Section: Related Workmentioning

confidence: 99%

“…Through experimental comparative analysis, the detection accuracy of the new method for rice diseases reached more than 95%. This shows that the MSSAE model has outstanding recognition performance for real crop disease image recognition 2 . But in the image processing stage, he extracts disease features in different color spaces, and then performs image segmentation and other operations on the disease.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, deep learning methods have been widely used in rice disease identification, but the actual image background of rice diseases is complex, and the classification performance is not ideal. Therefore, Meng et al 2 proposed a multi-scale feature extraction method based on stacked autoencoders. It is named as multi-scale stacked autoencoder to improve the recognition accuracy of rice diseases.…”

Section: Related Workmentioning

confidence: 99%

“…It uses data augmentation to increase the amount of sample data and improve the generalization ability of training results. (2) Using the original experimental algorithm in the laboratory and the deep belief network in the deep learning combined with the particle swarm optimization algorithm and the support vector machine to perform feature recognition on the images of rice sheath blight disease. The best feature combination is determined to improve the identification accuracy of rice sheath blight.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent collection of rice disease images based on convolutional neural network and feature matching

Yang

Zhang

2022

J. Electron. Imag.

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. Rapid and accurate determination of rice diseases is the key to accurate monitoring of agricultural conditions and timely prevention of agricultural yield reduction. Aiming at the problems of low identification accuracy of rice blast and low efficiency of manual detection, we propose an identification method for the six main diseases of rice in the natural environment. At the same time, the identification algorithm of rice false smut is studied. The purpose is to improve the efficiency of the intelligent collection of rice disease images based on convolutional neural network (CNN) and feature matching, so as to identify rice diseases faster. We propose to use SURF and HOG for feature extraction respectively. Then, 20 models were selected using K-nearest neighbors and support vector machine, respectively, for experiments using cross-validation. At the same time, residual network (ResNet) was used to conduct experimental research on six diseases of rice, and the samples were expanded and enhanced. The image scale is increased, and ResNet-20 is used to train the sample data. The experimental model achieved 96.7% accuracy on the test set. The new network achieved 97.4% accuracy. It is nearly 8% points higher than traditional image feature extraction methods. The experimental results show that the improved CNN has high accuracy and a good effect in the identification of main rice diseases.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent collection of rice disease images based on convolutional neural network and feature matching

Yang

Zhang

2022

J. Electron. Imag.

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“…Second, in the microbial images, the di erence degree between the image target and background greyscale is small. For multitarget microbial images, the spatial distribution of microorganisms is crossed and overlapping, and conventional segmentation techniques may produce a pseudocontour phenomenon [4]. In addition, noise is another important factor a ecting the imaging e ect of microbial images.…”

Section: Introductionmentioning

confidence: 99%

Multithreshold Microbial Image Segmentation Using Improved Deep Reinforcement Learning

Zhou¹

2022

Mathematical Problems in Engineering

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Image segmentation technology can effectively extract the foreground target in the image. However, the microbial image is easily disturbed by noise, its greyscale has the characteristics of nonuniform distribution, and several microorganisms with diverse forms exist in the same image, resulting in insufficient accuracy of microbial image segmentation. Therefore, a multithreshold microbial image segmentation algorithm using improved deep reinforcement learning is proposed. The wavelet transform method is used to remove the noise of the microbial image, the threshold number of the microbial image after denoising is determined by calculating the number of peaks of the grey histogram, and the foreground target of the microbial image is enhanced by the mean iterative threshold segmentation method, the preliminary segmentation of the microbial image is realized, the multithreshold microbial image segmentation model based on ResNet-Unet is constructed, and the cavity convolution and dual Q network mechanism are introduced to improve the segmentation model. The preliminary segmented microbial image is input into the improved segmentation model to realize the segmentation of the multithreshold microbial image. The results show that the proposed algorithm can effectively remove the noise of microbial images. With the increase in the number of thresholds, the peak signal-to-noise ratio, structural similarity, and feature similarity show an upward trend, and the loss rate of the model is less than 0.05%. The minimum running time of the algorithm is 3.804 s. It can effectively and quickly segment multithreshold microbial images and has important application value in the field of microbial recognition.

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Identification of paddy blast disease field images using multi-layer CNN models

Yakkundimath

Saunshi

2023

Environ Monit Assess

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Farmers and agricultural experts can take action on many areas of paddy crop handling and management practices with the use of actionable information from the in-eld diagnosis of paddy blast disease. To successfully diagnose the blast disease affecting fteen different paddy crop varieties, three transfer learning multi-layer convolutional neural network (CNN) models, such as, CapsNet, E cientNet-B7, and ResNet-50 are presented in this paper. The eld images of blast disease are captured and classi ed based on disease severity levels, such as low, medium, high, and severe. The study employing the CapsNet model with dataset consisting a total of 20,000 labeled images demonstrate the signi cant results with the testing e ciency of 90.79% and validation e ciency of 93.29%. The ResNet-50 and E cientNet-B7 models have yielded the average testing e ciencies of 85.10% and 88.72%, respectively. On the held out blast disease affected paddy eld image dataset, the CapsNet model outperformed the E cientNet-B7 and ResNet-50 CNN models related to both classi cation e ciency and computational e ciency.

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Rice Disease Image Recognition Based on Improved Multi-scale Stack Autoencoder

Cited by 5 publications

References 2 publications

Intelligent collection of rice disease images based on convolutional neural network and feature matching

Intelligent collection of rice disease images based on convolutional neural network and feature matching

Multithreshold Microbial Image Segmentation Using Improved Deep Reinforcement Learning

Identification of paddy blast disease field images using multi-layer CNN models

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