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Hillson Ghimire

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Articles

Wheat spike and spikelet detection and counting from high-resolution digital imagery using YOLO with Oriented Bounding Boxes

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By Maitiniyazi Maimaitijiang, Subash Thapa, Mandeep Singh, Santosh Subedi, Sunish Sehgal, Hillson Ghimire, Swas Kaushal, Dante Jerome Koupal, Mohammad Maruf Billah, Kushal Poudel, Shaurya Sehgal
| Nature Verified
Abstract In-season estimation of wheat grain yield potential is critical for crop management and advancing breeding efforts. Spike and spikelet counts serve as key indicators directly linked to yield potential, yet their assessment still relies on manual counting which is both labor-intensive and error-prone. High-resolution digital (RGB) imagery combined with deep learning-based object detection methods has substantially advanced automatic wheat spike detection and counting.
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