基于迁移学习的卷积神经网络花生荚果等级图像识别

针对花生荚果人工分级效率低、机械分级不精确等问题，该研究提出一种基于迁移学习的卷积神经网络花生荚果等级图像识别方法。利用翻转、旋转、平移、对比度变换和亮度变换等操作，对获取的5个等级花生荚果图像进行数量扩充和预处理，得到花生荚果等级图像数据集；对比分析了GoogLeNet、ResNet18和AlexNet 3种基本模型下花生荚果图像分级识别的性能；通过迁移AlexNet卷积层对花生荚果等级识别模型进行了改进，用批归一化替换局部响应归一化且将激活函数置于批归一化层前后不同位置，设计了4种不同的识别训练模型；对改进的4种AlexNet模型进行迁移学习对比试验和超参数学习率优化试验，研究了非饱和激活函数和改进的非饱和激活函数对模型性能的影响。试验结果表明，在满足测试精度的基础上AlexNet模型所用训练时间最少；基于AlexNet的改进模型的迁移学习中学习率是需要优化的超参数，合适的学习率能够加快模型的训练并提升识别能力；改进模型中批归一化的引入及网络参数的减少，缩减了220 s训练时间，模型性能提高。所构建的花生荚果等级识别模型（Penut_AlexNet model，PA模型）对花生荚果5个等级分类识别准确率达到95.43%，该模型对花生荚果等级识别具有较高的准确率，也可为其他农产品精确分级提供参考。

Image recognition of peanut pod grades based on transfer learning with convolutional neural network

Aiming at the problems of low efficiency of manual grading and inaccurate mechanical grading of peanut pods, a convolutional neural network peanut pod grades image recognition method based on transfer learning was proposed. By using the operations of the flip, rotation, translation, contrast transformation, and brightness transformation, the obtained five grades (first-grade pod, second-grade pod, third-grade pod, fourth-grade abnormal pod, and fifth-grade damaged pod) of peanut pod images were expanded and preprocessed, thus the peanut pod grades image data set was established. The 60% of data was randomly selected as the training set, 20% of data was randomly selected as the validation set, and the remaining 20% as the test set. The performance of peanut pod image classification based on the GoogLeNet, ResNet18, and AlexNet was compared and analyzed. The peanut pod grades recognition model was improved by transferring the AlexNet convolution layers. The local response normalization was replaced by batch normalization, and the activation function was placed in different positions before and after the batch normalization layer, so that four different recognition-training models were designed, including the PA-I model, PA-II model, PA-III model, and PA-IV model. The transfer learning contrast experiments and the hyperparameter optimization experiments of the learning rate carried out for the four improved AlexNet models proposed above. The effects of the unsaturated activation function (ReLU) and improved unsaturated activation function (LReLU) on the performance of the model were studied. The experimental results showed that the training time of the AlexNet model was the least on the basis of satisfying the test accuracy and the learning rate of transfer learning based on the improved AlexNet model was a very important hyperparameter that needed to be optimized. If the learning rate is chosen too high, the model training oscillates seriously and even can''t train normally; if the learning rate too small, the model training slow. An appropriate learning rate can speed up the training and improve the recognition ability of the model. When the learning rate was updated automatically, the model with batch normalization had better performance than local response normalization, which could make the model get higher accuracy and lower loss value. When the coefficient of activation function LReLU was 0.000 1, the performance of the LReLU used in the model was equivalent to that of the ReLU used in the model, therefore LReLU had no substantial impact on the training results of the model. The addition of batch normalization and reduction of parameters in the model reduced 220 s training time and improved the model''s performance. The classification accuracy of the proposed peanut pod grades recognition model for the first-grade pod, second-grade pod, third-grade pod, fourth-grade abnormal pod, and fifth-grade damaged pod was 93.57%, 97.14%, 99.29%, 87.14%, and 100% respectively and the average classification accuracy reached 95.43%, and F1 scores achieved 96.32%, 97.49%, 99.64%, 92.42%, and 94.50% respectively. The model proposed in this study had high classification accuracy for peanut pod grades and could provide a reference for the precise classification of other agricultural products.