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Using Deep Learning for Image-Based Plant Disease Detection

机译：使用深度学习进行基于图像的植物病害检测

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Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
机译：作物病害是对粮食安全的主要威胁，但由于缺乏必要的基础设施，在世界许多地方，很难迅速发现病害。深度学习使全球智能手机普及率不断提高和计算机视觉的最新发展相结合，为智能手机辅助疾病诊断铺平了道路。使用在受控条件下收集的54306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，以识别14种作物物种和26种疾病（或不存在）。经过训练的模型在保留的测试集上达到了99.35％的精度，证明了这种方法的可行性。总体而言，在越来越大且可公开获得的图像数据集上训练深度学习模型的方法为大规模的智能手机辅助作物病害诊断提供了一条清晰的道路。

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