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Image-based on-site detection technology for rice blast relies on prior knowledge which is affected by computational power and field network conditions, rendering adaptive real-time detection impossible. To tackle these challenges, a Mask R-CNN (Mask region-based convolutional neural network) model for rapid, high-throughput, and adaptive identification of rice blast was proposed. This model can be accelerated by using field programmable gate array (FPGA). Firstly, the backbone network was replaced with MobileNetV2, leveraging its inverted residual module to decrease computations and enhance the model’s parallel processing capabilities. Following that, a feature pyramid network module was incorporated to facilitate multi-scale feature fusion for rice blast, enabling the model to possess multi-scale adaptive processing abilities. Finally, the fully convolutional network(FCN) branch outputed the instance segmentation of rice blast lesions, utilizing the Softmax function to accurately localize and classify rice blast diseases. The validation results of the model using test datasets for rice blast disease demonstrated significant capabilities: when the input was a full HD image, the average inference time of the model was reduced to 85ms, which was 86.2% and 63.0% faster than the GPU server and the same level GPU edge computing platform, respectively. When the intersection over union ratio was 0.6, the accuracy can reach 98.0%, and the disease spot capture ability was improved by 21.2% on average. The Mask R-CNN adaptive fast identification model proposedcan realize the rapid field detection of rice blast disease under severe network conditions, and had better anti-noise ability and robust performance, which provided an efficient real-time system-on-chip scheme for real-time detection, inspection and mitigation of rice disease.