Hybrid improved capuchin search algorithm for plant image thresholding

Li, Shujing; Li, Zhangfei; Li, Qinghe; Zhang, Mingyu; Li, Linguo

doi:10.3389/fpls.2023.1122788

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…Exact optimization techniques use a continuous search strategy. It is based on the derivative of an objective function (Li et al, 2023;Mahmoodabadi et al, 2014;Ye et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Improved discrete particle swarm optimization using Bee Algorithm and multi-parent crossover method (Case study: Allocation problem and benchmark functions)

Zibaei,

Mesgari

2024

Preprint

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Compared to other techniques, particle swarm optimization is more frequently utilized because of its ease of use and low variability. However, it is complicated to find the best possible solution in the search space in large-scale optimization problems. Moreover, changing algorithm variables does not influence algorithm convergence much. The PSO algorithm can be combined with other algorithms. It can use their advantages and operators to solve this problem. Therefore, this paper proposes the onlooker multi-parent crossover discrete particle swarm optimization (OMPCDPSO). To improve the efficiency of the DPSO algorithm, we utilized multi-parent crossover on the best solutions. We performed an independent and intensive neighborhood search using the onlooker bees of the bee algorithm. The algorithm uses onlooker bees and crossover. They do local search (exploitation) and global search (exploration). Each of these searches is among the best solutions (employed bees). The proposed algorithm was tested on the allocation problem, which is an NP-hard optimization problem. Also, we used two types of simulated data. They were used to test the scalability and complexity of the better algorithm. Also, fourteen 2D test functions and thirteen 30D test functions were used. They also used twenty IEEE CEC2005 benchmark functions to test the efficiency of OMPCDPSO. Also, to test OMPCDPSO's performance, we compared it to four new binary optimization algorithms and three classic ones. The results show that the OMPCDPSO version had high capability. It performed better than other algorithms. The developed algorithm in this research (OMCDPSO) in 36 test functions out of 47 (76.60%) is better than other algorithms. The OMPCDPSO algorithm used many parts of the best solution. It put them in the multi-parent crossover and neighborhood search with onlookers. This made it better than DPSO. The Onlooker bees and multi-parent operators significantly impact the algorithm's performance.

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“…Exact optimization techniques use a continuous search strategy. It is based on the derivative of an objective function (Li et al, 2023;Mahmoodabadi et al, 2014;Ye et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Improved discrete particle swarm optimization using Bee Algorithm and multi-parent crossover method (Case study: Allocation problem and benchmark functions)

Zibaei,

Mesgari

2024

Preprint

View full text Add to dashboard Cite

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“…On the other hand, plants can use resistance strategies to prevent insect herbivory. When plants are attacked by insect herbivory, the nutrients, secondary metabolites, defense enzymes, and plant hormones in their bodies can cause an increase in the content of defense related compounds in the plant, reducing the insect's feeding preference, and even delaying or reducing the development of insects after feeding, thereby affecting the population size of insects (Yactayo-Chang et al 2021; Li et al 2023). Among them, secondary metabolites are one of the most direct connections between plants and insects, and the changes in secondary metabolites can be used as an evaluation of plant insect resistance (e.g., total phenols, tannins, and avonoids).…”

Section: Introductionmentioning

confidence: 99%

Response Strategies of Five Common Warm Temperate Species to Insect Defoliation

Wang,

Li,

et al. 2024

Preprint

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Under the background of global climate change, the insect herbivory phenomenon has been frequent, which significantly affects the growth, survival, and regeneration of forest plants in the warm temperate zone of China. Plants can adopt defense responses to adapt to insect defoliation. Therefore, field experiments were conducted on five common warm temperate species, Quercus acutissima, Quercus serrata, Quercus aliena, Quercus dentata, and Robinia pseudoacacia. We measured the leaf traits of healthy trees and insect defoliated trees, to explore the response strategies of common species in warm temperate zones to insect defoliation. Our results showed that native species stored more carbon in extreme environments for survival rather than growth, while alien species tended to adopt active resource acquisition strategies and were more inclined towards growth. Then the content of tannins and flavonoids in the alien species R. pseudoacacia did not significantly increase after leaf damage, while the content of secondary metabolites such as tannins, flavonoids, and total phenols in the native species Q. acutissima, Q. serrata, Q. aliena, Q. dentata increased significantly after leaf damage. This indicated that compared to alien species, native species would invest more resources in defense, which might reduce resource allocation for growth. Thus, the native Quercus species have stronger resistance than the alien species R. pseudoacacia after insect defoliation.

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“…The mAP of the new model was improved by 2.55% compared to YOLOv5, and its detection speed and effectiveness could meet the requirements of real-time weed monitoring in the field. In [28], the authors constructed a weed detection model called YOLOv7-FWeed based on YOLOv7. The new model improved the accuracy of weed identification using an F-ReLU and MaxPool multi-head self-attention module.…”

Section: Introductionmentioning

confidence: 99%

RVDR-YOLOv8: A Weed Target Detection Model Based on Improved YOLOv8

Ding,

Jiang,

Song

et al. 2024

Electronics

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Currently, weed control robots that can accurately identify weeds and carry out removal work are gradually replacing traditional chemical weed control techniques. However, the computational and storage resources of the core processing equipment of weeding robots are limited. Aiming at the current problems of high computation and the high number of model parameters in weeding robots, this paper proposes a lightweight weed target detection model based on the improved YOLOv8 (You Only Look Once Version 8), called RVDR-YOLOv8 (Reversible Column Dilation-wise Residual). First, the backbone network is reconstructed based on RevCol (Reversible Column Networks). The unique reversible columnar structure of the new backbone network not only reduces the computational volume but also improves the model generalisation ability. Second, the C2fDWR module is designed using Dilation-wise Residual and integrated with the reconstructed backbone network, which improves the adaptive ability of the new backbone network RVDR and enhances the model’s recognition accuracy for occluded targets. Again, GSConv is introduced at the neck end instead of traditional convolution to reduce the complexity of computation and network structure while ensuring the model recognition accuracy. Finally, InnerMPDIoU is designed by combining MPDIoU with InnerIoU to improve the prediction accuracy of the model. The experimental results show that the computational complexity of the new model is reduced by 35.8%, the number of parameters is reduced by 35.4% and the model size is reduced by 30.2%, while the mAP50 and mAP50-95 values are improved by 1.7% and 1.1%, respectively, compared to YOLOv8. The overall performance of the new model is improved compared to models such as Faster R-CNN, SSD and RetinaNet. The new model proposed in this paper can achieve the accurate identification of weeds in farmland under the condition of limited hardware resources, which provides theoretical and technical support for the effective control of weeds in farmland.

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Hybrid improved capuchin search algorithm for plant image thresholding

Cited by 7 publications

References 41 publications

Improved discrete particle swarm optimization using Bee Algorithm and multi-parent crossover method (Case study: Allocation problem and benchmark functions)

Improved discrete particle swarm optimization using Bee Algorithm and multi-parent crossover method (Case study: Allocation problem and benchmark functions)

Response Strategies of Five Common Warm Temperate Species to Insect Defoliation

RVDR-YOLOv8: A Weed Target Detection Model Based on Improved YOLOv8

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