植物叶片病害的深度学习识别

Milon Rana, Tajkuruna Akter Tithy, Nefaur Rahman Mamun, Hridoy Kumar Sharker
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引用次数: 1

摘要

作物病害对植物的生存构成重大威胁,但由于缺乏所需的基础设施,在地球上许多地方,快速识别病害仍然很困难。在计算机视觉领域,通过深度学习实现的植物叶片检测为智能手机辅助疾病诊断铺平了道路。利用在受控条件下收集的4306张患病和健康植物叶片图像的公共数据集,我们训练了一个深度卷积神经网络来识别一种作物物种和4种疾病(或不存在疾病)。经过训练的模型在hold -out测试集上的准确率达到了97.35%,证明了该方法的可行性。总的来说,在越来越大的、公开的图像数据集上训练深度学习模型的方法,为智能手机在全球范围内辅助作物疾病诊断提供了一条透明的道路。在以适当的置信度成功地预测疾病之后,就会显示针对该疾病的相应治疗方法,可以作为治疗方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plant Leaf Diseases Identification in Deep Learning
Crop diseases constitute a big threat to plant existence, but their rapid identification remains difficult in many parts of the planet because of the shortage of the required infrastructure. In computer vision, plant leaf detection made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. employing a public dataset of 4,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to spot one crop species and 4 diseases (or absence thereof). The trained model achieves an accuracy of 97.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of coaching deep learning models on increasingly large and publicly available image datasets presents a transparent path toward smartphoneassisted crop disease diagnosis on a large global scale. After the disease is successfully predicted with a decent confidence level, the corresponding remedy for the disease present is displayed that may be taken as a cure.
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