YOLO11_Opt: An Ultra-Lightweight Improved YOLO11n Algorithm for Low-Cost Embedded Devices for Accurate Plant Disease Detection—A Case Study on Bell Pepper

Abstract

The early and accurate detection of plant diseases is essential for crop management and agricultural loss control, especially under resource limitations. We propose an optimized YOLO11n architecture, designated as YOLO11_Opt, targeting real-time inference on low-cost embedded systems. The model is computationally efficient through the selective narrowing of its width and depth, while performing competitively in two-class object recognition tasks. Pepper leaves were chosen as the materials for study. Three methods of quantization (FP32, FP16, and INT8) were investigated. After running the experiments, the results showed that YOLO11_Opt greatly reduces the computational complexity: the complexity decreased from 6.3 GFLOPS and 2.58 million parameters in the typical YOLO11n model to a very small 0.5 GFLOPS and 0.33 million parameters, while maintaining competitive detection capabilities. The improved FP32 model has a mAP (0.5:0.95) of 0.913 and a precision of 0.991, while the old version has 0.961 mAP and 0.996 precision. Lastly, implementations on embedded hardware prove that the method is feasible: the detection accuracy of the system in live classification is around 92% with Raspberry Pi 4 and 94% with NVIDIA Jetson Nano, with inference times of as little as 1.9 ms on NVIDIA Jetson Nano and 8.3 ms on Raspberry Pi 4. Thus, YOLO11_Opt demonstrates significant potential as a reliable, high-performance, low-cost solution to identifying plant diseases on devices in precision agriculture.