Deep Learning-Based Multistage Peach Ripeness Detection with Data Leakage Mitigation and Real-World Validation

Accurate fruit ripeness assessment is essential for optimizing harvest timing and ensuring postharvest quality, particularly in climacteric fruits such as peaches, which exhibit rapid and heterogeneous ripening. This study proposes a deep learning-based approach for multistage peach ripeness classif...

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Bibliografiska uppgifter

Huvudupphov:	Salvador Castro-Tapia, Germán Díaz-Florez, Rafael Reveles-Martínez, Héctor A. Guerrero-Osuna, Luis F. Luque-Vega, Humberto Morales-Magallanes, Jorge Pablo Vega-Borrego, Gilberto Vázquez-García, Carlos A. Olvera-Olvera
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	MDPI AG 2026-05-01
Serie:	Applied Sciences
Ämnen:	peach ripeness detection AlexNet EfficientNet YOLOv8 YOLOv11 YOLOv12
Länkar:	https://www.mdpi.com/2076-3417/16/9/4484
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