Computer vision for Ethiopian agricultural crop pest identification

Alemayehu, Dagnachew Melesew; Mengistu, Abrham Debasu; Mengistu, Seffi Gebeyehu

doi:10.11591/ijeecs.v3.i1.pp209-214

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…In the experiment, the mean and variance of the input variables were changed to generate the target data set, D t , and multiple source data sets, D i s . By adjusting the size of the mean, the similarity between the target field and the source field could be controlled, where each feature variable of D t obeyed the [0,3] Gaussian distribution, the number of training samples generated was 200, and the number of test samples was 100, by setting the mean value of a feature variable x i to between [20,30], and the variance to [1,3]. Then, we generate different source domain data sets.…”

Section: Martial Arts Action Sample Datamentioning

confidence: 99%

“…Wireless Communications and Mobile Computing number of samples was 1000. To compare the transfer effects of different methods, all samples in the source field were used to assist training, and the number of training samples in the target field was gradually increased for multiple trials [20]. In the data collection stage, sensors could be used to sample martial arts movements to obtain upper limb movement information and lower limb movement information; we performed data preprocessing on the upper limb movement information and lower limb movement information to obtain the preprocessed data information.…”

Section: Martial Arts Action Sample Datamentioning

confidence: 99%

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Influence of Embedded Microprocessor Wireless Communication and Computer Vision in Wushu Competition Referees’ Decision Support

Liang

2022

Wireless Communications and Mobile Computing

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With the development of computer technology and management science, decision support systems have emerged that can improve the quality and effects of decision-making. This study mainly examined the application of the wireless communication of an embedded microprocessor and computer vision in the decision support system of martial arts competition referees. Using the embedded microprocessor’s characteristics of a small size, high precision, high reliability, and high efficiency, a decision support system for martial arts competition referees was designed. In the experiment, the similarity between the target field and the source field could be controlled by adjusting the mean value. To better extract the target, this study used the time domain changes of the three adjacent frames, before, middle, and back, to detect the moving target to extract the change detection template. The Canny edge detection method was used to extract the edge information of the image and eliminate the nonmotion area; then, morphology was used to correct the image to complete the connection of the broken edge to obtain the final initial segmentation mask image. In the process of calculation, there were some noises and small fragments. In this study, morphology and background difference were used to optimize the segmented image. Experimental data show that the algorithm detection accuracy rate was high—between 70% and 100%—and the effect was relatively ideal. The results indicate that the proposed algorithm can effectively reduce matching noise, improve the matching accuracy of the edge area and the low-texture area, and achieve a fast matching speed.

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Section: Martial Arts Action Sample Datamentioning

confidence: 99%

Section: Martial Arts Action Sample Datamentioning

confidence: 99%

Influence of Embedded Microprocessor Wireless Communication and Computer Vision in Wushu Competition Referees’ Decision Support

Liang

2022

Wireless Communications and Mobile Computing

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“…Modifikasi dan kombinasi RBF dengan metode lainnya dapat dilakukan untuk menyelesaikan masalah titik pusat tersebut, misalnya menggunakan data terdiskresi [6], support vector machine (SVM) [7], dan self organizing map (SOM) [8]- [13]. Modifikasi RBF dapat dilakukan dengan menggunakan keluaran dari SOM.…”

Section: Pendahuluanunclassified

Combination of SOM-RBF for drought code prediction using rainfall and air temperature data

Midyanti

2019

Jurnal Teknologi dan Sistem Komputer

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This study aims to predict Drought Code (DC) in Kabupaten Kubu Raya using a combination of SOM-RBF. The final weight value of SOM was used as a center on the RBF network. The input data variables are rainfall data and air temperature data for three days with three binary outputs to predict DC values. This study also observed the effect of the number of neurons, learning rates, and the number of iterations on the results of the SOM-RBF network training. The smallest MSE of training result from the SOM-RBF network was 0.159933 using 65 neurons in the hidden layer, learning rate 0.007, and epoch 45000. The detection accuracy of SOM-RBF was 91.34 % from 245 test data.

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“…In [2] the authors elaborated a multilayer perceptron-based technology to real time detection of weeds in growing onions. In [3] a hybrid of support vector machine (SVM) with Radial basis function (RBF) are used to identification of four major types of crop pest which occurs on teff, wheat, sorghum, barley and maize. In [4] the authors proposes a novel angular texture pattern (ATP) extraction method for crop and weed discrimination to overcome the defectsof background complexity and variation of illumination in the agricultural scene.…”

Section: Introductionmentioning

confidence: 99%

Real Time Weed Detection using a Boosted Cascade of Simple Features

Tannouche¹,

Sbai²,

Rahmoune³

et al. 2016

IJECE

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<p>Weed detection is a crucial issue in precision agriculture. In computer vision, variety of techniques are developed to detect, identify and locate weeds in different cultures. In this article, we present a real-time new weed detection method, through an embedded monocular vision. Our approach is based on the use of a cascade of discriminative classifiers formed by the Haar-like features. The quality of the results determines the validity of our approach, and opens the way to new horizons in weed detection.</p>

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Computer vision for Ethiopian agricultural crop pest identification

Abstract: Crop pest is an organism that creates damage on to the agriculture by feeding crops.

Cited by 5 publications

References 1 publication

Influence of Embedded Microprocessor Wireless Communication and Computer Vision in Wushu Competition Referees’ Decision Support

Influence of Embedded Microprocessor Wireless Communication and Computer Vision in Wushu Competition Referees’ Decision Support

Combination of SOM-RBF for drought code prediction using rainfall and air temperature data

Real Time Weed Detection using a Boosted Cascade of Simple Features

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