Attention-Based Target Detection–You Only Look Once: A Detection Model for <i>Locusta migratoria</i> ssp. <i>manilensis</i> in Complex Environments
Locusts have always been among the important hazards affecting crop growth and the grassland ecological environment. Accurate and timely detection of locusts is crucial for effective control of insect development. Aiming at the problem of false detection and missed detection caused by locust occlusi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/6/1381 |
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| author | Peng Wang Jiandong Fang Xiuling Wang Yudong Zhao |
| author_facet | Peng Wang Jiandong Fang Xiuling Wang Yudong Zhao |
| author_sort | Peng Wang |
| collection | DOAJ |
| description | Locusts have always been among the important hazards affecting crop growth and the grassland ecological environment. Accurate and timely detection of locusts is crucial for effective control of insect development. Aiming at the problem of false detection and missed detection caused by locust occlusion and background similarity in complex field environments, this paper proposes a lightweight Attention-based Target Detection (ATD) model while constructing the dataset Real-Locust with the theme of <i>Locusta migratoria</i> ssp. <i>manilensis</i>. By introducing to attention mechanism and lightweight design, the model achieves a mean average precision (mAP) of 90.9% on the Real-Locust dataset, and the precision and recall rate are increased by 0.6% and 4.3%, respectively. At the same time, the number of parameters and computational complexity are reduced by 27.4% and 22.9%, showing that this provides an efficient solution for real-time monitoring of locusts. |
| format | Article |
| id | doaj-art-5cdde3129ed24419a0cc790a4d3e05a3 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-5cdde3129ed24419a0cc790a4d3e05a32025-08-20T03:30:25ZengMDPI AGAgronomy2073-43952025-06-01156138110.3390/agronomy15061381Attention-Based Target Detection–You Only Look Once: A Detection Model for <i>Locusta migratoria</i> ssp. <i>manilensis</i> in Complex EnvironmentsPeng Wang0Jiandong Fang1Xiuling Wang2Yudong Zhao3College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaInner Mongolia Key Laboratory of Intelligent Perception and System Engineering, Hohhot 010080, ChinaLocusts have always been among the important hazards affecting crop growth and the grassland ecological environment. Accurate and timely detection of locusts is crucial for effective control of insect development. Aiming at the problem of false detection and missed detection caused by locust occlusion and background similarity in complex field environments, this paper proposes a lightweight Attention-based Target Detection (ATD) model while constructing the dataset Real-Locust with the theme of <i>Locusta migratoria</i> ssp. <i>manilensis</i>. By introducing to attention mechanism and lightweight design, the model achieves a mean average precision (mAP) of 90.9% on the Real-Locust dataset, and the precision and recall rate are increased by 0.6% and 4.3%, respectively. At the same time, the number of parameters and computational complexity are reduced by 27.4% and 22.9%, showing that this provides an efficient solution for real-time monitoring of locusts.https://www.mdpi.com/2073-4395/15/6/1381locust detectioncomplex environmentmulti-scale targetYOLOv8deep learning |
| spellingShingle | Peng Wang Jiandong Fang Xiuling Wang Yudong Zhao Attention-Based Target Detection–You Only Look Once: A Detection Model for <i>Locusta migratoria</i> ssp. <i>manilensis</i> in Complex Environments Agronomy locust detection complex environment multi-scale target YOLOv8 deep learning |
| title | Attention-Based Target Detection–You Only Look Once: A Detection Model for <i>Locusta migratoria</i> ssp. <i>manilensis</i> in Complex Environments |
| title_full | Attention-Based Target Detection–You Only Look Once: A Detection Model for <i>Locusta migratoria</i> ssp. <i>manilensis</i> in Complex Environments |
| title_fullStr | Attention-Based Target Detection–You Only Look Once: A Detection Model for <i>Locusta migratoria</i> ssp. <i>manilensis</i> in Complex Environments |
| title_full_unstemmed | Attention-Based Target Detection–You Only Look Once: A Detection Model for <i>Locusta migratoria</i> ssp. <i>manilensis</i> in Complex Environments |
| title_short | Attention-Based Target Detection–You Only Look Once: A Detection Model for <i>Locusta migratoria</i> ssp. <i>manilensis</i> in Complex Environments |
| title_sort | attention based target detection you only look once a detection model for i locusta migratoria i ssp i manilensis i in complex environments |
| topic | locust detection complex environment multi-scale target YOLOv8 deep learning |
| url | https://www.mdpi.com/2073-4395/15/6/1381 |
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