用卷积神经网络检测芙蓉花的健康状况

Devesh Kumar Srivastava, Dharmendra Narayan Jha
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引用次数: 0

摘要

随着生产和销售结合现代技术和工艺改进,印度的花卉市场正在取得巨大飞跃。花卉种植面临着收获后损失的问题,高达20%至35%,迫使农民解决这一领域的问题。重点是根据其健康状况和感染状况对产品进行分类。农民可以将其交付给合适的客户,以最大限度地减少损失并提高效率。本文主要研究了健康木槿花的检测及其提取工艺。鲜花是生产出口优质木槿油的关键。明确的意图是跟踪木槿花的寿命,通过应用图像分类和深度学习等机器学习技术,对花的健康状况进行判断,并识别感染花(如果检测到)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hibiscus Flower Health Detection to Produce Oil Using Convolution Neural Network
Flower market is taking a big leap in India as production and marketing is incorporating modern technologies and process improvements Flower Farming aka floriculture is facing the problem of post-harvest losses, up to 20 to 35% and forcing to address the solution concerns of farmers in this field. The focus is to classify the product, based on its health till infected condition. Farmers can deliver it to appropriate customers' leads to minimize the losses and increasing efficiency. This paper focuses on detection of healthy hibiscus flower along with its oil extraction process. The fresh flower plays especially key role to produce export quality hibiscus oil. The clear intent is to track the lifespan of hibiscus flower, where judgment of health of the flowers are addressed along with identification of infectious flowers (if detected) by applying Machine Learning Techniques like image classification and deep learning.
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