Parallelization of the Honeybee Search Algorithm for Object Tracking

Perez-Cham, Oscar E.; Puente, Cesar; Soubervielle‐Montalvo, Carlos; Olague, Gustavo; Aguirre-Salado, Carlos Arturo; Núñez-Varela, Alberto S.

doi:10.3390/app10062122

Cited by 15 publications

(19 citation statements)

References 58 publications

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“…In object tracking, swarm intelligence and evolutionary algorithms have been shown to perform well. In Perez-Cham et al [33], a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles named Honeybee Search Algorithm was proposed which was inspired by the search for food of honeybees. A key element of this work was the capability to run the algorithm in parallel on the GPU accelerating the processing time.…”

Section: Alternative Architecturesmentioning

confidence: 99%

Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

Storey

Meng

2022

Sustainability

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Reduction in chemical usage for crop management due to the environmental and health issues is a key area in achieving sustainable agricultural practices. One area in which this can be achieved is through the development of intelligent spraying systems which can identify the target for example crop disease or weeds allowing for precise spraying reducing chemical usage. Artificial intelligence and computer vision has the potential to be applied for the precise detection and classification of crops. In this paper, a study is presented that uses instance segmentation for the task of leaf and rust disease detection in apple orchards using Mask R-CNN. Three different Mask R-CNN network backbones (ResNet-50, MobileNetV3-Large and MobileNetV3-Large-Mobile) are trained and evaluated for the tasks of object detection, segmentation and disease detection. Segmentation masks on a subset of the Plant Pathology Challenge 2020 database are annotated by the authors, and these are used for the training and evaluation of the proposed Mask R-CNN based models. The study highlights that a Mask R-CNN model with a ResNet-50 backbone provides good accuracy for the task, particularly in the detection of very small rust disease objects on the leaves.

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Section: Alternative Architecturesmentioning

confidence: 99%

Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

Storey

Meng

2022

Sustainability

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“…The communication method used by honeybees inspired the work of [54] for 3D reconstruction which is robust to outliers. The authors of [55] used to swarm behaviour of the Honeybee Search Algorithm (HSA) for object tracking.…”

Section: Literature Surveymentioning

confidence: 99%

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

et al. 2020

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Feature selection is a process to reduce the dimension of a dataset by removing redundant features, and to use the optimal subset of features for machine learning or data mining algorithms. This helps to minimize the time requirement to train a learning algorithm as well as to lessen the storage requirement by ignoring the less-informative features. Feature selection can be considered as a combinatorial optimization problem. In this paper, the authors have presented a new feature selection algorithm called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm and Harmony Search. Mayfly Algorithm has not hitherto been used for feature selection problems to the best of the author's knowledge. An S-shaped transfer function is incorporated for converting it into a binary version of Mayfly Algorithm. When different candidate solutions obtained from various regions of the search space using Mayfly Algorithm are taken into the harmony memory and processed by Harmony Search, a superior solution can be ensured. This is the primary reason for proposing a hybrid of Mayfly Algorithm and Harmony Search. Thus, combining harmony search with Mayfly Algorithm leads to an increased exploitation of the search space and an overall improvement in the performance of Mayfly-Harmony Search (MA-HS) algorithm. The proposed algorithm has been applied on 18 UCI datasets and compared with 12 other state-of-the-art meta-heuristic FS methods. Experiments have also been performed on three high-dimensional microarray datasets. The results obtained support the superior performance of the algorithm compared to the other methods. The source code of the proposed algorithm can be found using the link as follows: https://github.com/trin07/MA-HS.

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“…is the i-th individual in the store matrix MEM;  is the diversity threshold, as shown in (18); rand is a random number on [0,1];  is the control parameter, the initial value is 1, and it is updated according to (19) at the end of each generation;  is the parameter of crowding degree, as shown in (20).…”

Section: Population Reproduction Under the Guidance Of Pioneer Insectmentioning

confidence: 99%

“…It can solve the problems of complexity and uncertainty in real engineering problems. Therefore, swarm intelligence evolution algorithms have been widely used in many engineering fields, including complex network community detection [13], image processing [14], pattern recognition [15], spectrum sensing [16], 3D scene reconstruction [17], object tracking [18], and wireless sensor networks [19]. That also makes the swarm intelligence evolution algorithm has been a relatively active research direction, and its related research mainly focuses on the following two aspects: First, aiming at the defects of the existing algorithm, propose improved algorithms to solve practical problems; Second, proposing a new swarm intelligence evolution algorithm to solve practical problems.…”

Section: Introductionmentioning

confidence: 99%