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Optimizing tomato detection and counting in smart greenhouses: A lightweight YOLOv8 model incorporating high- and low-frequency feature transformer structures

Zhimin Tian, Huijuan Hao, Guowei Dai, Jun Li

Year
2024
Citations
4

Abstract

between predicted and actual counts is 0.9282, indicating the algorithm's suitability for replacing manual counting. This method effectively supports tomato detection and counting in intelligent greenhouses, offering valuable insights for robotic harvesting and yield estimation research.

Keywords

TransformerFeature (linguistics)Computer scienceGreenhouseArtificial intelligenceLow frequencyPattern recognition (psychology)EngineeringElectrical engineeringTelecommunications

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