深度学习检测辣椒植株营养缺乏

A. Bahtiar, Pranowo, A. Santoso, Jujuk Juhariah
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引用次数: 16

摘要

由于市场需求旺盛,辣椒是一种主要商品,也影响着印尼的经济。2019年6月证实,辣椒是印尼通货膨胀率从0.55%上升到0.20%的原因之一。其中一个因素是营养不良导致作物歉收。在这项研究中,目的是探索农业中的深度学习技术,帮助农民诊断他们的植物,使他们的植物不会营养不良。采用RCNN算法作为本系统的架构。使用4个类别的270个数据集。使用的数据集是印度尼西亚Boyolali Regency辣椒样本的原始数据。我们用的辣椒是卷辣椒。本研究的结果是,计算机能够根据接收到的图像输入识别辣椒植株的营养缺乏,测试精度最高为82.61%,mAP值最佳为15.57%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Detected Nutrient Deficiency in Chili Plant
Chili is a staple commodity that also affects the Indonesian economy due to high market demand. Proven in June 2019, chili is a contributor to Indonesia’s inflation of 0.20% from 0.55%. One factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their plants, so that their plants are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency, Indonesia. The chili we use are curly chili. The results of this study are computers that can recognize nutrient deficiencies in chili plants based on image input received with the greatest testing accuracy of 82.61% and has the best mAP value of 15.57%.
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