Detection of Diseases and Pests on The Leaves of Sweet Potato Plants sing Yolov4

Melita Nisti, Aviv Yuniar Rahman, Fitri Marisa
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Abstract

Sweet potato (Ipomea batats) is a root plant that can live in all weather, in mountainous areas and on the coast.. This plant is one of the important food crops in Indonesia, and makes Indonesia the second largest sweet potato producer after China. However, according to data from the Central Statistics Agency (BPS), sweet potato production in Indonesia in 2018 decreased by 5.63% when compared to production in 2017 which reached 1,914,244 tons (Gultom, 2021). Based on these data, it is important to conduct research on pest and disease detection in plants. Therefore, the author conducted a study related to this problem entitled Detection of Diseases and Pests on the Leaves of Sweet Potato Plants using Yolov4 with the aim of helping educate farmers in recognizing diseases on the leaves of sweet potato plants and how to overcome them. In this study the dataset was sweet potato leaves with a total of 1500 data divided into three classes, namely aspidomorpha, yellow spot and normal leaves with 4000 iterations. The best training results on 1500 data with 75% accuracy. The Yolov4 algorithm produces high accuracy in detecting diseases in the leaves of sweet potato plants.  
检测甘薯叶片上的病虫害 sing Yolov4
甘薯(Ipomea batats)是一种根茎植物,可以在任何天气下、山区和沿海地区生存。这种植物是印尼重要的粮食作物之一,使印尼成为仅次于中国的第二大红薯生产国。然而,根据中央统计局(BPS)的数据,2018 年印尼的甘薯产量比 2017 年减少了 5.63%,2017 年的产量达到 1,914,244 吨(Gultom,2021 年)。基于这些数据,开展植物病虫害检测研究非常重要。因此,作者开展了一项与此问题相关的研究,题为 "使用 Yolov4 检测甘薯植株叶片上的病虫害",旨在帮助教育农民识别甘薯植株叶片上的病害以及如何克服这些病害。这项研究的数据集是甘薯叶片,共有 1500 个数据,分为三类,即aspidomorpha、黄斑和正常叶片,迭代次数为 4000 次。1500 个数据的训练结果最好,准确率为 75%。Yolov4 算法检测甘薯叶片病害的准确率很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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