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Abstract：
Aiming at the problems that the traditional image recognition technology is challenging to extract useful features and the recognition time is extended; the AlexNet model is improved to improve the effect of image classification and recognition. This study focuses on 8 types of tomato leaf diseases and healthy leaves. By using HOG and LBP weighted fusion to extract image features, a tomato leaf disease recognition model based on the AlexNet model is proposed, and transfer learning is used to train the AlexNet model. Transfer the knowledge learned by the AlexNet model on the PlantVillage image dataset to this model while reducing the number of fully connected layers. Keras deep learning framework and programming language Python were used. The model was implemented, and the classification and identification of tomato leaf diseases were carried out. The recognition rate of feature-weighted fusion classification is higher than that of serial and parallel methods, and the recognition time is the shortest. When the weight coefficient ratio of HOG and LBP is 3:7, the image recognition rate is the highest, and its value is 97.2 %. From the model performance curve See, when the number of iterations is more than 150 times, the training set and test accuracy rate both exceed 97 %, the loss rate shows a gradient decline, and the change is relatively stable; compared with the traditional AlexNet model, HOG + LBP + SVM model, and VGG model, improved AlexNet model has the highest recognition rate, and it has high recall value, accuracy, and F1 value; Compared with the latest convolutional neural network disease recognition models, improved AlexNet model recognition accuracy was 98.83 %, and the F1 value was 0.994. It shows that the model has good convergence performance, fast prediction speed, and low loss rate and can effectively identify 8 types of tomato leaf images, which provides a reference for the research on crop disease identification.
摘要：
针对传统图像识别技术难以提取有用特征和延长识别时间的问题,对AlexNet模型进行了改进,提高了图像分类识别的效果。本研究重点研究了8种番茄叶病和健康叶片。利用HOG和LBP加权融合提取图像特征,提出了一种基于AlexNet模型的番茄叶部病害识别模型,迁移学习用于训练AlexNet模型。将AlexNet模型在PlantVillage图像数据集上学到的知识转移到此模型，同时减少完全连接的层数。使用Keras深度学习框架和编程语言Python。该模型已实现，并对番茄叶部病害进行了分类鉴定。特征加权融合分类的识别率高于串行和并行,识别时间最短。当HOG和LBP的权重系数比为3:7时,图像识别率最高,其值为97.2%。从模型性能曲线看，当迭代次数超过150次时，训练集和测试准确率均超过97%，损失率呈梯度下降，并且变化相对稳定；与传统的AlexNet模型相比，HOG+LBP+SVM模型,和VGG模型，改进的AlexNet模型具有最高的识别率，它具有很高的召回价值，准确度,和F1值;与最新的卷积神经网络疾病识别模型相比,改进的AlexNet模型识别准确率为98.83%，F1值为0.994。结果表明,该模型具有良好的收敛性能,预测速度快，且损失率低，能有效识别8种番茄叶片图像，为作物病害识别研究提供参考。
