Guo Ziyu, Yin Yong, Gu Haolin, Peng Guihua, Wang Xueya, Chen Ju, Yan Jia
College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
Chili Pepper Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China.
Foods. 2025 Jul 25;14(15):2612. doi: 10.3390/foods14152612.
A chili pepper variety and origin detection system that integrates a field-programmable gate array (FPGA) with an electronic nose (e-nose) is proposed in this paper to address the issues of variety confusion and origin ambiguity in the chili pepper market. The system uses the AIRSENSE PEN3 e-nose from Germany to collect gas data from thirteen different varieties of chili peppers and two specific varieties of chili peppers originating from seven different regions. Model training is conducted via the proposed lightweight convolutional neural network ChiliPCNN. By combining the strengths of a convolutional neural network (CNN) and a multilayer perceptron (MLP), the ChiliPCNN model achieves an efficient and accurate classification process, requiring only 268 parameters for chili pepper variety identification and 244 parameters for origin tracing, with 364 floating-point operations (FLOPs) and 340 FLOPs, respectively. The experimental results demonstrate that, compared with other advanced deep learning methods, the ChiliPCNN has superior classification performance and good stability. Specifically, ChiliPCNN achieves accuracy rates of 94.62% in chili pepper variety identification and 93.41% in origin tracing tasks involving Jiaoyang No. 6, with accuracy rates reaching as high as 99.07% for Xianjiao No. 301. These results fully validate the effectiveness of the model. To further increase the detection speed of the ChiliPCNN, its acceleration circuit is designed on the Xilinx Zynq7020 FPGA from the United States and optimized via fixed-point arithmetic and loop unrolling strategies. The optimized circuit reduces the latency to 5600 ns and consumes only 1.755 W of power, significantly improving the resource utilization rate and processing speed of the model. This system not only achieves rapid and accurate chili pepper variety and origin detection but also provides an efficient and reliable intelligent agricultural management solution, which is highly important for promoting the development of agricultural automation and intelligence.
本文提出了一种将现场可编程门阵列（FPGA）与电子鼻（e-nose）相结合的辣椒品种和产地检测系统，以解决辣椒市场中品种混淆和产地不明的问题。该系统使用德国的AIRSENSE PEN3电子鼻收集来自13个不同品种辣椒以及源自7个不同地区的2个特定品种辣椒的气体数据。通过所提出的轻量级卷积神经网络ChiliPCNN进行模型训练。ChiliPCNN模型结合了卷积神经网络（CNN）和多层感知器（MLP）的优势，实现了高效且准确的分类过程，辣椒品种识别仅需268个参数，产地溯源需244个参数，分别具有364次浮点运算（FLOPs）和340次FLOPs。实验结果表明，与其他先进的深度学习方法相比，ChiliPCNN具有卓越的分类性能和良好的稳定性。具体而言，在涉及6号椒的辣椒品种识别任务中，ChiliPCNN的准确率达到94.62%，在产地溯源任务中的准确率达到93.41%，对于301线椒，准确率高达99.07%。这些结果充分验证了该模型的有效性。为了进一步提高ChiliPCNN的检测速度，在美国赛灵思Zynq7020 FPGA上设计了其加速电路，并通过定点运算和循环展开策略进行了优化。优化后的电路将延迟降低到5600纳秒，功耗仅为1.755瓦，显著提高了模型的资源利用率和处理速度。该系统不仅实现了辣椒品种和产地的快速准确检测，还提供了一种高效可靠的智能农业管理解决方案，对推动农业自动化和智能化发展具有重要意义。
