论文标题
使用转移学习的葡萄疾病检测
Grapes disease detection using transfer learning
论文作者
论文摘要
植物中疾病的早期和精确诊断可以帮助发展早期治疗技术。植物疾病降低了农作物的数量和质量，从而对粮食安全构成威胁，并造成了巨大的经济损失。传统上，手动进行识别，这是不准确，耗时且昂贵的。本文提出了一个简单有效的模型，可以使用转移学习检测葡萄叶疾病。预先训练的深卷积神经网络用作特征提取器和随机森林作为分类器。模型的性能是根据准确性，精度，回忆和F1分数来解释的。总共使用了四个不同类别的1003张图像，并获得了91.66％的精度。
Early and precise diagnosis of diseases in plants can help to develop an early treatment technique. Plant diseases degrade both the quantity and quality of crops, thus posing a threat to food security and resulting in huge economic losses. Traditionally identification is performed manually, which is inaccurate, time-consuming, and expensive. This paper presents a simple and efficient model to detect grapes leaf diseases using transfer learning. A pre-trained deep convolutional neural network is used as a feature extractor and random forest as a classifier. The performance of the model is interpreted in terms of accuracy, precision, recall, and f1 score. Total 1003 images of four different classes are used and 91.66% accuracy is obtained.
