Proceedings
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| Filter results2 paper(s) found. |
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1. Evaluating the Impact of Vegetation Indices on Plant Nitrogen Uptake Prediction: a Comparative Study of Regression Models at Various Growth StagesNitrogen and water play crucial roles in impacting both the health and yield of corn crops. However, their demands vary under different soil and weather conditions. Unfortunately, current nitrogen management practices in irrigated fields in the state of Georgia overlook this variability. Thus, this oversight may lead to insufficient nitrogen application, causing plant stress or excessive nitrogen application that can lead to environmental impact. To address this challenge, a precise assessment... B. Ghimire, L. Lacerda, T. Bourlai |
2. Evaluating Deep Learning Models for Image-Based Corn Kernel Detection, Counting and Yield PredictionAccurate estimation of kernel number in corn is essential for evaluating yield potential in breeding and agronomic research. However, manual kernel counting is labor-intensive, prone to human error, and impractical for large-scale datasets, while most existing automated devices are limited to simple counting tasks. This study evaluates deep learning-based approaches for automated kernel detection and counting using You Only Look Once models and Faster R-CNN. Specifically, YOLOv8x, YOLOv10x, and... B. Ghimire, L. Lacerda, T. Bourlai, G. Lu |