Enhanced SHADE for Drone Control in a Swarm-Intelligent Pollination System.
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| Title: | Enhanced SHADE for Drone Control in a Swarm-Intelligent Pollination System. |
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| Authors: | Hasegawa, Daigo, Shindo, Takuya, Hiraguri, Takefumi, Itoh, Nobuhiko |
| Source: | Journal of Signal Processing (1342-6230); Jul2025, Vol. 29 Issue 4, p123-126, 4p |
| Subject Terms: | EVOLUTIONARY algorithms, BIOLOGICAL evolution, SWARM intelligence, SEARCH algorithms, ENERGY consumption, POLLINATION, DIFFERENTIAL evolution, BEES algorithm |
| Abstract: | Bumblebees are often used as pollinators in greenhouse cultivation. However, they become less active in hot environments, such as during summer. Additionally, the rising procurement costs owing to the mass mortality of bumblebees have become a significant issue. To address these challenges, we propose an artificial pollination system that uses small drones as an alternative to bees. In this study, we integrate the success-history-based adaptive differential evolution (SHADE) algorithm into the proposed system to optimize the drone's path control and reduce power consumption during flower searching. The algorithm is modified to enhance its suitability for the proposed system. It is specifically designed to minimize hovering time, a key factor in drone power consumption. Furthermore, a simulated greenhouse environment was used to determine whether the drones employed swarm intelligence or an evolutionary algorithm for flower searching. The enhanced SHADE algorithm, adapted for this method, enables efficient and accurate flower detection while significantly reducing energy consumption compared to conventional SHADE and differential evolution algorithms. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Signal Processing (1342-6230) is the property of Research Institute of Signal Processing, Japan and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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