基于人工智能和边缘计算的YOLOv5作物检测深度学习模型

S. Bhavan, Mohana
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引用次数: 0

摘要

越来越多的公司正致力于机器人技术的进步,以制造无人机、自动拖拉机、机器人收割机、自动灌溉和播种机器人。根据论文和研究,这个问题可以利用机器学习和深度学习方法来解决。虽然一些文章声称使用正确的相机可以提高模型的准确性,但这高度依赖于作物和地理条件,如阳光和地形。本文提出了一种综合利用边缘计算和深度学习对农作物进行二值分类的方法。与之前召回率不超过92%的调查结果相比,这款车型的召回率攀升至99%。边缘计算和人工智能有可能改变农业。使用边缘计算机可以大大减少时间、成本和劳动力,从而间接增加产出。所开发的应用程序对改进模型很有帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
YOLOv5 Crop Detection Deep Learning Model using Artificial Intelligence (AI) and Edge Computing
A rising number of firms are working on robotics advancements to create drones, autonomous tractors, robotic harvesters, automated irrigation, and seeding robots. According to papers and research, this problem can be solved utilizing machine learning and deep learning approaches. While some articles claim that employing the correct cameras can improve model accuracy, this is highly reliant on crop and geographical conditions such as sunshine and terrain. This paper suggests a comprehensive method to use edge computing and deep learning to perform binary classification on crops. The model's recall climbed to 99 percent when compared to previous findings, when the recall did not exceed 92 percent. Edge computing and artificial intelligence have the potential to transform agriculture. The usage of Edge computers may greatly cut time, cost, and labour, hence increasing output indirectly. The application developed proved to be useful in improving the model.
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