نبذة مختصرة : Accurate identification of diseases and insect pests has been the key link to the yield, quality and safety of crops in modern agriculture. However, the same category of diseases and insect pests showed obvious differences in intra-class representation and slight similarities in inter-class representation of various diseases, due to the influence of environmental conditions, disease cycle and damaged tissues. At present, the traditional deep transfer learning methods have been difficult to cope with large-scale, multi-class fine-grained identification of pests and diseases, particularly unsuitable to the practice in complicated scenes. This paper aims to apply multiple source cameras in agricultural Internet of Things (IoT) and different intelligent precision equipment, including picking robots and smart phones, to capture high resolution 122,000 images of insect pests and diseases, covering a total of 181 fine-grained categories, including 49 types pests and 77 types diseases on different plant parts of different crops. A fine-grained recognition model was then proposed for pests and diseases based on the Multi-Stream Gaussian Probability Fusion Network (MPFN). In detail, a data-augmented method was first employed to enlarge the dataset, and then to pretrain the basic VGG19 and ResNet networks on high-quality images, in order to learn common and domain knowledge, as well fine-tuning with professional skill. Next, the refined multiple deep learning networks, including Fast-MPN and NTS-Net with transfer learning, were applied to design a multi-stream feature extractor, utilizing the mixture-granularity information to exploit high-dimensionality features, thereby to distinguish interclass discrepancy and tolerate intra-class variances. Finally, an integrated optimization was developed combining the NetVLAD feature aggregation layer with the gaussian probability fusion layer, in order to fuse various components model with Gaussian distribution as a unified probability representation for the ultimate fine-grained recognition. The input of this module was the various features of multi-models, whereas, the output was the fused classification probability. The end-to-end implementation of framework included an inner loop about the expectation maximization algorithm within an outer loop with the gradient back-propagation optimization of the whole network, indicating multi-model fusion information complementation and confidence for the overall model. The experimental results demonstrated that the MPFN model presented the excellent performance in the average recognition accuracy rate of 93.18% for a total of 181 classes of pests and diseases, indicating 5.6 percentage points better than that of the coarse-grained and fine-grained deep learning methods. In terms of test time, the average processing time of the MPFN was 61ms, indicating the basic needs of fine-grained image recognition of pests and diseases at the terminals of the IoT and intelligent equipment. In the contradistinctive analysis, training loss curves and various sub-categories identification showed that the feature aggregation fusion and Gaussian probability fusion can greatly enhance the efficiency of model with the training speed, recognition accuracy and generalized robustness for fine-grained identification of crop pests and diseases in practical scenarios. Therefore, this work can provide a technical application reference for the intelligent recognition of pests and diseases in agricultural production. In the follow-up study, more images will be taken under natural conditions to develop a more robust MPFN model deployed on actual IoTs or equipment for the pre-warning, prevention, and control of crop pests and diseases in modern agriculture. [ABSTRACT FROM AUTHOR]
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