Field-Programmable Gate Arrays (FPGAs) are key platforms for edge computing and have broad potential in edge-side image recognition. This paper presents an embedded system based on a domestically produced FPGA and a self-designed ASIC-like architecture for real-time edge deployment. On the software side, a lightweight neural network named NexusEdgeNet is proposed. It achieves 94.22% accuracy on 39 farmland disease categories with only 0.184 MB of parameters. On the hardware side, an ASIC-like accelerator fully described in Verilog is designed. It adopts a distributed on-chip memory structure, eliminating external memory access, and supports arbitrary-shaped convolution, pooling, and fully connected operations. Several optimization techniques are applied, including near-memory parallel computing, pipelining, sliding convolution windows, and double buffering. The accelerator reaches 399 FPS inference speed on the EP6HL130 FPGA, with 85% resource utilization and significantly reduced logic consumption. The system integrates image acquisition, processing, and display, supporting real-time video stream recognition. It maintains high accuracy while achieving excellent real-time performance and resource efficiency. This work provides a practical, low-cost solution for edge computing applications based on domestic FPGAs.