Search

The symptoms of the same tomato disease in different stages are obviously different, and different diseases show some similarities. Traditional pattern recognition methods can not reflect the dynamic changes of the pathological characterization, and the practicability is poor. To solve this problem, this paper proposed a Multi-Scale AlexNet recognition model for mobile platform based on convolutional neural network (CNN), and implemented a tomato leaf disease image recognition system for agricultural workers based on Android. Many parameters and large memory utilization of traditional AlexNet model are unfit for mobile platform, this paper adjusted the network structure of the traditional model by removing the local response normalization(LRN) layer, modifying the full connection layer, setting up different convolution kernel extraction features, designed a multi- scale recognition model based on the AlexNet. The model consists of 6 layers. It can optimize the training time and memory utilization and achieve high precision. After removing the LRN layer, there was a 30% decrease in running time. Extending the single convolution kernel into multi-scale (1 × 1,3 × 3,5 × 5,7 × 7) convolution kernel then fused at the first layer, removing full connection layer 6 and 7, and taking the place of global average pooling layer, then the model size was only 30.2 M. The forward propagation rate (FRP) and backward propagation rate (BPR) were reduced, and the global average pooling is better than the global maximum pooling on recognition accuracy. So the Multi-Scale AlexNet model used global average pooling in the 5th layer. In image preprocessing phase, in order to avoiding over fitting of the trained model caused by the unbalanced distribution of sample numbers, we had zoomed, flipped, color jittering, add noise and rotated the original pictures of dataset randomly to get the augmented dataset, and used 70% of the pictures as the train dataset and the rest as the validation dataset(20%) and test dataset(10%). These pictures were quantized to 224 × 224 dpi for Multi-Scale AlexNet training, and the original dataset and augmented dataset were used to train models. In order to validate the performance of the proposed model, comparative tests were done between Multi-Scale AlexNet and traditional pattern recognition method. It repeated 600 tests. The results showed that the CNN model achieved 92.7%, the high average recognition accuracy of each disease and each disease in the early, middle and late stages. Compared with the other CNN Net model(MobileNet, SequeezeNet, LeNet-5), the Multi-Scale AlexNet achieved the highest recognition accuracy, and reached 95.8% on the late stage disease dataset, but the SequeezeNet model used less memory. The MobileNet and SequeezeNet model reached lower recognition accuracy on the middle and late stages dataset, that because their convolution size was small. The recognition system was implemented on Android platform, and then test was done on field dataset. The results showed that the average recognition accuracy was 89.2%, its less value was due to the complex background of image. Then the system can meet the requirements of disease image recognition on mobile platform in production practice. The research results provide a method for disease image recognition based on convolution neural network, and provide a reference for automatic identification of crop diseases and engineering applications. [ABSTRACT FROM AUTHOR]

China is the largest country for cultivating kiwifruit, and Shaanxi Province provides the largest production, which accounts for approximately 70% of the production in China and 33% of the global production. Harvesting kiwifruit in this region relies mainly on manual picking which is labor-intensive. Therefore, the introduction of robotic harvesting is highly desirable and suitable. The fast and effective recognition of kiwifruit in the field under natural scenes is one of the key technologies for robotic harvesting. Recently, the study on kiwifruit recognition has been limited to a single cluster and multi clusters in the field have seldom been considered. In this paper, according to growth characteristics of kiwifruit grown on sturdy support structures, an RGB (red, green, blue) camera was placed around 100 cm underneath the canopy so that kiwifruit clusters could be included in the images. We proposed a kiwifruit image recognition system based on the convolutional neural network (CNN), which has a good robustness avoiding the subjectivity and limitation of the features selection by artificial means. The CNN could be trained end to end, from raw pixels to ultimate categories, and we optimized the critical structure parameters and the training strategy. Ultimately, the network was made up of 1 input layer, 3 convolutional layers, 2 sub-sampling layers, 1 full convolutional layer, and 1 output layer. The CNN architecture was optimized by using batch normalization (BN) method, which normalized the data distribution of the middle layer and the output data, accelerating the training convergence and reducing the training time. Therefore, the BN layers were added after the 1, 3 and 5th convolutional layer (Conv1, Conv3, and Conv5 layer) of the original LeNet network. The size of all convolutional kernels was 5×5, and that of all the sub-sampling layers was 2×2. The feature map numbers of Conv1, Conv3, and Conv5 were 6, 16 and 120, respectively. After manual selection and normalizing, the RGB image of kiwifruit was transferred into a matrix with the size of 32×32 as the input of the network, stochastic gradient descent was used to train our models with mini-batch size of 100 examples, and momentum was set as 0.9. In addition, the CNN took advantages of the part connections, the weight sharing and Max pooling techniques to lower complexity and improve the training performance of the model simultaneously. The network used rectified linear units (ReLU) as activation function, which could greatly accelerate network convergence. The proposed model for training kiwifruit was represented as 32×32-6C-2S-16C-2S-120C-2. Finally, 100 images of kiwifruit in the field (including 5918 fruits) were used to test the model, and the results showed that the recognition ratios of occluded fruit, overlapped fruit, adjacent fruit and separated fruit were 78.97%, 83.11%, 91.01% and 94.78%, respectively. The overall recognition rate of the model reached 89.29%, and it only took 0.27 s in average to recognize a fruit. There was no overlap between the testing samples and the training samples, which indicated that the network had a high generalization performance, and the testing images were captured from 9 a.m. to 5 p.m., which indicated the network had a good robustness to lightness variations. However, some fruits were wrongly detected and undetected, which included the fruits occluded by branches or leaves, overlapped to each other and the ones under extremely strong sunlight. Particularly, 2 or more fruits overlapped were recognized as one fruit, which was the main reason to the success rate not very high. This phenomenon demands a further research. By comparing with the conventional methods, it suggested that the method proposed obtained a higher recognition rate and better speed, and especially it could simultaneously identify multi-cluster kiwifruit in the field, which provided significant support for multi-arm operation of harvesting robotic. It proves that the CNN has a great potential for recognition of fruits in the field. [ABSTRACT FROM AUTHOR]