采用改进的SqueezeNet模型识别多类叶片病害

为实现作物叶片病害的准确识别,该研究以PlantVillage工程开源数据库中14种作物38类叶片为研究对象,从网络规模小型化和计算过程轻量化需求的角度出发,对经典轻量级卷积神经网络SqueezeNet提出改进措施,包括修改最后一层卷积层的输出、删除经典模型中的后3个fire模块并修改fire模块5的参数、调节fire模块中expand层中1×1和3×3的卷积核数目的比例、移动部分fire模块在模型中的位置等措施,共获取5种改进的病害叶片检测模型,并运用迁移学习和随机梯度下降算法进行训练。试验结果表明,在不过多损失网络性能的前提下,改进后5种模型的参数内存需求及模型计算量均呈现大幅减小,模型收敛迅速,其中最优模型参数内存需求仅为0.62MB,模型运算量仅为111MFLOPs,其平均准确率达到98.13%,平均查全率达到98.09%,平均查准率达到97.62%,在与已有相关研究的对比中表现出较高的性价比。该研究提出的改进模型在大幅减少参数内存要求和计算量的同时使模型性能保持在一个较高的水平,较好地平衡了这3项指标,适合将模型部署在移动终端等嵌入式资源受限设备上,有助于实现对作物病害的实时准确识别。

Identification of multiple leaf diseases using improved SqueezeNet model

Abstract: A significant increase in agricultural production is highly demanding, due to the ever-increasing human population over the last decades. However, the crops yield is greatly affected by various plant diseases. A timely and accurate identification of leaf disease is very necessary for plant disease control. In this study, 38 types of leaves images (from 14 different crops) were used for the identification. These images were collected from the PlantVillage project (an open source leaf disease database). Since the convolutional neural network (CNN) can automatically learn appropriate features from training data, the CNN has become one of the most popular ways for image identification, better than the traditional machine learning using manual feature extraction. However, the number of parameters in a CNN model was very huge, leading to a very heavy computation load. Thus, the traditional CNN was difficult to apply in real time measurement. Three methods were proposed to reduce the size of a CNN model and the computation load. The first method was to replace some of the 3×3 convolution filter with 1×1 convolution filter. A 3×3 convolution filter included 9 parameters, requiring 9 floating point multiplications to obtain one solution. If this 3×3 convolution filter was replaced by a 1×1 convolution filter, the number of parameters and the number of required multiplications can reduce to 1. Thus, this method can help to reduce the size of a model and its computation load. The second method was to move the convolution calculation from large- to small-size feature maps. The idea was to reduce the computation load at the cost of a slightly performance drop. This can be done by adjusting the position of convolution module in the CNN. If a 3×3 filter was convolute with a 2N×2N feature map, the total number of floating point multiplication was 36N2. If the same 3×3 filter was convolute with a N×N feature map, the total number of floating point multiplication was 9N2, only a quarter of previous computation load. Therefore, if the convolution was performed with a smaller size feature map, the amount of calculation greatly reduced. The premise of this modification was that the performance drop was very small. The third method was to reduce the depth of a CNN model. It was obvious that no need a very deep neural network for a relatively simple task. Therefore, the idea behind the third method was to use a suitable network instead of a very complicated neural network for a relatively simple task. The experiment showed that the performance drop in the SqueezeNet was only around 0.5% for a classification task in the 38 types of images, if removed the last 3 fire modules. 5 improved CNN models were proposed for the leaf disease identification. The experiments showed that the size of the optimal model was around 0.62MB, and the computation load of this model was only 111 MFLOPs. Specifically, the average accuracy rate was 98.13%, and the average recall rate was 98.09%, while the average precision rate was 97.62%, showing higher cost efficiency than before. The proposed model can greatly reduce the size of the model, while reducing the computation load, and only a slight decrease in performance. There was an excellent balance on the performance, model size, and computation load. The improved model can be suitable for deployment on mobile terminals and other embedded resource-constrained devices, thereby contribute to real-time and accurate identification of crop diseases.