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The grape industry has been one of the most important strategies in the rural revitalization and precise poverty alleviation. A serious disturbance to grape planting can be the pests and diseases in recent years. The refined measures of agricultural and biological control are highly required to effectively reduce the occurrence of pests and diseases for economic and safety concerns. Accurate identification and diagnosis of grape diseases and insect pests can be the premise of fine control actions. The traditional identification of pests and diseases relies mainly on the experts or technical personnel for the visual sightings, particularly with the time-consuming, laborious, high cost, low timeliness, and difficult to be widely used. Alternatively, machine learning and computer vision can be expected to serve the promising application of efficient image identification. There is also a great potential to accelerate the identification efficiency of pests and diseases, cost saving, and high accuracy. Machine learning classification includes the feature representation and classifier. However, the manual feature-based identification can only be suitable for the small data sets at present. Most machine learning algorithms also depend on complex image processing and hand-designed features, resulting in low robustness in pests and disease identification, especially in a complex environment. Fortunately, deep learning can be widely expected in the agricultural field, due to the most cutting-edge, modern, and promising technology in recent years. This study aims to treat the small data set and a large number of model parameters in the existing deep learning for higher identification accuracy. A data set of grape pests and diseases was also constructed, containing the healthy leaves, three types of diseased leaves, and 16 types of insect pests. An identification model was then proposed for the grape pests and diseases using the improved MobileNet V2. Firstly, a Coordinate Attention (CA) was embedded in the reverse residual module of MobileNet V2 to enhance the information representation of the model. Secondly, a two-branch feature fusion module was designed using depthwise separable convolution for the better identification performance of the model. Finally, the number of channels was adjusted to streamline the structure of MobileNet V2. As such, a lightweight identification model (named MobileNet_Vitis) was proposed for the grape pests and diseases. The results show that the better performance of the model with the CA module was achieved after 1×1 pointwise convolution. Specifically, the accuracy of the improved model was enhanced by 1.61 percentage points, while the parameter size was reduced by 1.62 MB, compared with the introducing CA module after 3×3 depthwise convolution. The two-branch feature fusion also greatly contributed to the higher identification accuracy, which was improved by 1.4 percentage points than before. In addition, a tradeoff of identification accuracy and speed was obtained to adjust the channels in the model. The identification accuracy and F1 score of MobileNet_Vitis on the dataset were 89.16% and 80.44%, respectively, which was 1.83 percentage points and 9.31 percentage points higher than that of MobileNet V2 before improvement, respectively. More importantly, the parameter size of MobileNet_Vitis was 7.85 MB, which was 8.5% less than that of MobileNet V2. Consequently, the MobileNet_Vitis presented a higher identification accuracy and F1 score with a smaller size of parameters, compared with the ResNet 101, ShuffleNet V2, MobileNet V3, and GhostNet. The inference time of MobileNet_Vitis for a single pest image was 17.53 ms, fully meeting the requirement of fast identification. The improved model can be widely expected to better identify the grape pests and diseases with less complexity. Therefore, the MobileNet_Vitis model can be further deployed to a mobile applet for the grape pests and disease control. [ABSTRACT FROM AUTHOR]

[Objectives] In view of the rapid development of facility fruit industry and the urgent need of early warning of diseases and insect pests in the hilly area of Jiangsu Province, a network service platform for early warning of fruit pest prevention and control was established according to the characteristics and laws of fruit pests in the hilly area. [Methods] A friendly human-computer interaction mode was constructed through SQL Server database and Web browser to realize visualization of fruit tree diseases and pests module, scientific medication guidance module and popular science knowledge module, and the reasoning mechanism of remote diagnosis module and network early warning module were realized through database and rule base, aiming at difficult diseases and pests at the same time. An expert answer module was set up. The common diseases and insect pests of grape, strawberry, pear and peach planted extensively in hilly area were early warned and prevented by remote monitoring. The functions of remote diagnosis, weather forecast, scientific medication guidance, popularization of planting knowledge and interactive exchange of experts were realized. [Results] The platform integrates disease and insect pest summary, remote diagnosis, network early warning and information consulting services. It can effectively improve the efficiency of fruit production and management in hilly areas of Jiangsu Province, improve the accuracy and efficiency of remote diagnosis of fruit diseases and insect pests, and reduce the cost of control. It can theoretically guide farmers to scientifically plant protection and medication. [Conclusions] The information platform for early warning of fruit tree diseases and insect pests can significantly help fruit growers to save water and medicine, and increase both production and income, which is worthy of further promotion. [ABSTRACT FROM AUTHOR]