Study on YOLOcF using different modules.

<div><p>Fruit detection using the YOLO framework has fostered fruit yield prediction, fruit harvesting automation, fruit quality control, fruit supply chain efficiency, smart fruit farming, labor cost reduction, and consumer convenience. Nevertheless, the factors that affect fruit detect...

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Bibliographic Details
Main Author:	He Honggang (22190472) (author)
Other Authors:	Olarewaju Mubashiru Lawal (14710251) (author), Yao Tan (519662) (author), Kui Cheng (2182816) (author)
Published:	2025
Subjects:	Medicine Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified target dense status study findings indicate labor cost reduction cfruit image dataset smart fruit farming fruit quality control fruit harvesting automation affect fruit detectors fruit detection using high computation costs div >< p 3 %, 0 1 %, 1 >< sup >< yolocf fruit detector 8 %, 1 yolov5 </ p computation costs fruit images 7 %, yolocf detector detection speed based yolov5 r </ params </ map </ gflops </ fps </ 2 </ yolo variants yolo framework still remain significant challenge low speed count analysis consumer convenience ></ sup >, exceeds 422 value
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Study on YOLOcF using different modules.

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