Study on Corn Disease Identification Based on PCA and SVM

Liu, Zixi; Du, Zhixiong; Peng, Yue; Tang, Ming; Liu, Xiaoyu; Chen, Weicong

doi:10.1109/itnec48623.2020.9084689

Cited by 17 publications

(10 citation statements)

References 2 publications

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“…In the (6), in case of an input image 𝑏 𝑡 the average value is expressed through 𝑛; further central AoP is achieved are encoded with the (7), where 𝑛 𝐽 indicates central pixels value.…”

Section: Figure 1 Corn Leaf Disease Typesmentioning

confidence: 99%

“…A fast and effective technique is required to assess the quality of corn kernels [6]. However, several leaf diseases occur in the corn crop, can heavily impact the quality of crop [7] as well as yield. Moreover, the production rate of the corn crop is also affected  ISSN: 2088-8708 Int J Elec & Comp Eng, Vol.…”

Section: Introductionmentioning

confidence: 99%

“…In [7], a corn disease identification method is adopted using the classification of leaf diseases based on support vector machine (SVM) classifier. A detailed study on segmentation of leaf diseases is also presented.…”

Section: Introductionmentioning

confidence: 99%

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Corn leaf image classification based on machine learning techniques for accurate leaf disease detection

Noola¹,

Basavaraju²

2022

IJECE

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<span lang="EN-GB">Corn leaf disease possesses a huge impact on the food industry and corn crop yield as corn is one of the essential and basic nutrition of human life especially to vegetarians and vegans. Hence it is obvious that the quality of corn has to be ideal, however, to achieve that it has to be protected from the several diseases. Thus, there is a high demand for an automated method, which can detect the disease in early-stage and take necessary steps. However, early disease detection possesses a huge challenge, and it is highly critical. Thus, in this research work, we focus on designing and developing enhanced-K nearest neighbour (EKNN) model by adopting the basic K nearest neighbour (KNN) model. EKNN helps in distinguishing the different class disease. Further fine and coarse features with high quality are generated to obtain the discriminative, boundary, pattern and structural related information and this information are used for classification procedure. Classification process provides the gradient-based features of high quality. Moreover, the proposed model is evaluated considering the Plant-Village dataset; also, a comparative analysis is carried out with different traditional classification model with different metrics.</span>

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Section: Figure 1 Corn Leaf Disease Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Corn leaf image classification based on machine learning techniques for accurate leaf disease detection

Noola¹,

Basavaraju²

2022

IJECE

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“…Over the past few years, the research of machine learning in the field of image recognition had been developed. Some researchers used support vector machine (SVM) to detect and classify images (Kaur et al, 2019;Liu et al, 2020). Zhang et al (2018a) used the K-means clustering algorithm to identify four cucumber leaf disease images and three apple leaf disease images.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet

Lü

Zhang²,

Zeng³

et al. 2022

Front. Plant Sci.

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In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to optimize the hyperparameters in the identification and classification model of rice leaf disease images, such as learning rate, training batch size, convolution kernel size and convolution kernel number. Firstly, the opposition-based learning is added to the whale population initialization with improving the diversity of population initialization. Then the algorithm improves the convergence factor, increases the weight coefficient, and calculates the self-mapping chaos. It makes the algorithm have a strong ability to find optimization in the early stage of iteration and fast convergence rate. And disturbance is carried out to avoid falling into local optimal solution in the late stage of iteration. Next, a polynomial mutation operator is introduced to correct the current optimal solution with a small probability, so that a better solution can be obtained in each iteration, thereby enhancing the optimization performance of the multimodal objective function. Finally, eight optimized performance benchmark functions are selected to evaluate the performance of the algorithm, the experiment results show that the proposed WOACW outperforms than 5 other common improved whale optimization algorithms. The WOACW_SimpleNet is used to identify rice leaf diseases (rice blast, bacterial leaf blight, brown spot disease, sheath blight and tungro disease), and the experiment results show that the identification average recognition accuracy rate reaches 99.35%, and the F1-score reaches 99.36%.

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