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  1. AI-powered detection and quantification of Post-harvest Physiological Deterioration (PPD) in cassava using YOLO foundation models and K-means clustering by Gomez Ayalde, Daniela, Giraldo Londono, Juan Camilo, Quiroga Mosquera, Audberto, Luna-Melendez, Jorge Luis, Gimode, Winnie, Tran, Thierry, Zhang, Xiaofei, Selvaraj, Michael Gomez

    Published 2024
    “…Although YOLO-NAS had some instability during training, it demonstrated stronger performance in detecting the PPD_0 class, with a mAP of 91.3%. YOLOv7 exhibited the lowest performance across all classes, with an overall mAP of 75.5%. …”
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    Journal Article
  2. Economic analysis of cross-breeding programmes in sub-Saharan Africa: A conceptual framework and Kenyan case study by Karugia, Joseph T., Okeyo Mwai, Ally, Kaitho, R.J., Wollny, C.B.A., Drucker, Adam G., Rege, J.E.O.

    Published 2001
    “…The two models were developed by the Impact Assessment Group (2000) of Texas A&M University, USA, and applied to evaluate the impact of improved dairy technologies in Kenya in collaboration with the Kenya Agricultural Research Institute (KARI) and the International Livestock Research Institute (ILRI). …”
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    Libro

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