{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:41:36Z","timestamp":1770918096153,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The counting of pineapple buds relies on target recognition in estimating pineapple yield using unmanned aerial vehicle (UAV) photography. This research proposes the SFHG-YOLO method, with YOLOv5s as the baseline, to address the practical needs of identifying small objects (pineapple buds) in UAV vision and the drawbacks of existing algorithms in terms of real-time performance and accuracy. Field pineapple buds are small objects that may be detected in high density using a lightweight network model. This model enhances spatial attention and adaptive context information fusion to increase detection accuracy and resilience. To construct the lightweight network model, the first step involves utilizing the coordinate attention module and MobileNetV3. Additionally, to fully leverage feature information across various levels and enhance perception skills for tiny objects, we developed both an enhanced spatial attention module and an adaptive context information fusion module. Experiments were conducted to validate the suggested algorithm\u2019s performance in detecting small objects. The SFHG-YOLO model exhibited significant gains in assessment measures, achieving mAP@0.5 and mAP@0.5:0.95 improvements of 7.4% and 31%, respectively, when compared to the baseline model YOLOv5s. Considering the model size and computational cost, the findings underscore the superior performance of the suggested technique in detecting high-density small items. This program offers a reliable detection approach for estimating pineapple yield by accurately identifying minute items.<\/jats:p>","DOI":"10.3390\/s23229242","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T09:23:43Z","timestamp":1700213023000},"page":"9242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["SFHG-YOLO: A Simple Real-Time Small-Object-Detection Method for Estimating Pineapple Yield from Unmanned Aerial Vehicles"],"prefix":"10.3390","volume":"23","author":[{"given":"Guoyan","family":"Yu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Guangdong Provincial Engineering Technology Research Center for Marine Equipment and Manufacturing, Zhanjiang 524088, China"},{"name":"Southern Laboratory of Marine Science and Engineering (Guangdong Province), Zhanjiang 524013, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6677-5466","authenticated-orcid":false,"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7512-6570","authenticated-orcid":false,"given":"Guoquan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China"}]},{"given":"Haochun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Guangdong Provincial Engineering Technology Research Center for Marine Equipment and Manufacturing, Zhanjiang 524088, China"},{"name":"Southern Laboratory of Marine Science and Engineering (Guangdong Province), Zhanjiang 524013, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s10681-022-03030-y","article-title":"Current status of pineapple breeding, industrial development, and genetics in China","volume":"218","author":"Li","year":"2022","journal-title":"Euphytica"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11831-021-09569-8","article-title":"Emerging trends in machine learning to predict crop yield and study its influential factors: A survey","volume":"29","author":"Bali","year":"2022","journal-title":"Arch. 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