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Abstract

Potato plants have a very high nutritional value, making them widely cultivated in Indonesia. To ensure the cultivation of potatoes has good quality, many individuals, ranging from farmers to researchers and plant breeders, strive to explore and understand the characteristics of plant resistance sources, one of which is through the role of trichomes. Trichomes are fine hairs that coat the outer surface of plant leaves, serving as a physical barrier and regulating plant temperature. Identification and quantification of trichomes are commonly conducted manually by researchers, which consumes much time and is inefficient. Therefore, a system that can automatically detect and quantify trichomes is crucial to avoid manual identification and quantification, allowing these processes to be carried out more quickly. This study utilized a deep learning approach to train a model capable of detecting and quantifying trichome objects. The model architecture used was YOLOv8. From the training process, the resulting mean average precision (mAP) at a confidence threshold of 50 was 0.816, while the mAP at a confidence threshold of 90 was 0.38. This model is expected to assist experts or researchers in the field of agriculture in identifying trichomes, thereby optimizing crop yields.

Keywords

glandular trichomes potato deep learning object detection YOLOv8

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How to Cite
Azhari, M. F., Rohmatul Fajriyah, Izzati Muhimmah, Dan Jeric Arcega Rustia, Smulders, M. J., & Gracianna Devi, M. (2024). Detection and Quantification of Glandular Trichomes (Bulbous) on Potato Plant Leaf Images Using Deep Learning. Enthusiastic : International Journal of Applied Statistics and Data Science, 4(2), 96–108. https://doi.org/10.20885/enthusiastic.vol4.iss2.art2
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