Artificial bee colony optimization (ABC) for grape leaves disease detection

Andrushia, A. Diana; Patricia, A. Trephena

doi:10.1007/s12530-019-09289-2

Cited by 51 publications

(13 citation statements)

References 30 publications

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“…In some related studies, ABC is used to optimize the text feature space [50], identify diseases in grape leaves [51] and optimize artificial neural networks [52]. In this paper, the combination of ABC and SVM is to predict the survival risk of esophageal cancer patients.…”

Section: Artificial Bee Colony-support Vectors Machinesmentioning

confidence: 99%

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

et al. 2020

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This article provides a method based on self-organizing maps (SOM) neural network clustering and support vector machine (SVM) ensembles to predict the survival risk levels of esophageal cancer. Nine blood indexes related to patient survival are found by using SOM clustering method. Two critical thresholds for survival are found by plotting the receiver operating characteristic (ROC) curve twice, and the lifetime is divided into three risk levels. Using the SVM method, patients' risk levels are predicted and assessed. Four kernel functions of SVM are compared, and the prediction effect of RBF kernel function is better than other kernel functions. The parameters of SVM are optimized by using genetic algorithm (GA), particle swarm algorithm (PSO) and artificial bee colony (ABC) algorithm. Experimental results show that the prediction accuracies are improved by using optimization algorithms. After comparison, ABC-SVM has better prediction results than GA-SVM and PSO-SVM with a high prediction rate and fast running time. INDEX TERMS artificial bee colony, genetic algorithm, particle swarm optimization, self-organizing maps, support vector machine.

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Section: Artificial Bee Colony-support Vectors Machinesmentioning

confidence: 99%

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

et al. 2020

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“…Recently, nature-inspired optimization has been widely used in image processing [21], text document clustering [22], industrial data mining [23] and decision-making problems [24]. The algorithms most commonly used in computer vision are ant colony [25], particle swarm [26], bee colony [27], Grey Wolf [28], and Cockroach Colony [29]. In 2019, Mohit et al [30] proposed a novel nature-inspired algorithm: Squirrel search.…”

Section: Heuristic Optimizationmentioning

confidence: 99%

Heuristic EPnP-Based Pose Estimation for Underground Machine Tracking

Zheng

Song

et al. 2022

Symmetry

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Pose estimation is one of the most complicated and compromising problems for underground mining machine tracking, and it is particularly important for hydraulic support autonomous following mining machine (AFM) policy-making system. In this paper, a low-cost infrared vision-based system through an Efficient Perspective-n-Point (EPnP) algorithm is proposed. To improve efficiency and simplify computation, a traditional EPnP algorithm is modified through a nature-inspired heuristic optimization algorithm. The optimized algorithm is integrated into the AFM policy-making system to estimate the relative pose (R-Pose) estimation between hydraulic support and the mining machine’s shearer drum. Simple yet effective numerical simulations and industrial experiments were carried out to validate the proposed method. The pose estimation error was ≤1% under normal lighting and illuminance, and ≤2% in a simulated underground environment, which was accurate enough to meet the needs of practical applications. Both numerical simulation and industrial experiment proved the superiority of the approach.

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“…In another study [12] Artificial bee colony (ABC) has been used to identify and classify diseases in grape leaves using the colony characteristics of artificial bees. After pre-processing and removing the noise of the leaves, the characteristics of texture, shape and color were extracted.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Leaf Surface Disease Using Two Datasets of Tomato and Rice Obtained from Image Processing Techniques

Hosseiny¹,

Jola²,

Hosseiny³

2021

RCES

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It is of a great importance in modern agriculture to study fast, automatic, inexpensive and accurate method of diagnosing plant diseasesTherefore, timely and accurately diagnosis of the disease in the fields is one of the most important factors in dealing with plant diseases. For this reason, in the present study, the image processing method study, has been examined for diagnosing the two important diseases of rice and tomato, brown spots and leaf blasts. In this study, firstly the data section is treated using improved k-means segmentation, after preprocessing. Secondly, comprehensive features are extracted and the disease areas are demarcated. An improved genetic algorithm is used in the feature selection step. Finally, images are categorized using the k-nearest neighbor’s algorithm (k-NN) classifier. The accuracy of the proposed method for the rice data set is 99.12 and for the tomato data set is 97.29, which shows a very good performance compared to other methods.

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Artificial bee colony optimization (ABC) for grape leaves disease detection

Cited by 51 publications

References 30 publications

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

Heuristic EPnP-Based Pose Estimation for Underground Machine Tracking

Diagnosis of Leaf Surface Disease Using Two Datasets of Tomato and Rice Obtained from Image Processing Techniques

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