Diagnosis of tomato pests and diseases based on super lightweight convolu-tional neural network
In the diagnosis of tomato diseases and pests,traditional convolutional neural network structures are com-plex and hard to be directly applied to portable terminals.Besides,existing lightweight convolutional neural networks exhibit weak feature extraction capabilities,low recognition accuracy,and are inadequate for practical applications.Aiming at the above problems,we intended to define a super lightweight convolutional neural network based on existing lightweight convo-lutional neural network,and to design an ultra-lightweight convolutional neural network by improving the SqueezeNet net-work for tomato disease and pest diagnosis tasks.Firstly,we enhanced the Fire module in the SqueezeNet network,generated two Fire modules suitable for different feature di-mensions.We introduced efficient channel attention(ECA)module to improve feature extraction capabilities of the model.Secondly,we incorporated scaled exponential linear unit(SELU)and Mish to replace rectified linear u-nit(ReLU)as activation function.Next,we employed Softpool instead of the original max pooling.Finally,we enhanced the exponential normalized loss(Softmax loss)by using Center loss function to improve the recognition accuracy of approximate diseases and pests.In this experiment,we selected eight types of pests and nine types of diseases to perform data augmentation on three datasets(pests,diseases,diseases and pests),and investigated the impact of small sample and data imbalance on model performance.Experimental results demonstrated that the network proposed in this study had super lightweight characteristics.The recognition accuracies for pests,diseases,and diseases and pests could reach up to 98.83%,98.14%and 97.71%,respectively,which met the requirements for diagnosis effectively.
image recognitiontomato pests and diseasessuper lightweight convolutional neural networkimbalance
NETL
NSTL
万方数据
维普
