Nyakuri Jean Pierre, Nkundineza Celestin, Gatera Omar, Nkurikiyeyezu Kizito, Mwitende Gervais
African Centre of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali, Rwanda.
Department of Mechanical and Energy Engineering, University of Rwanda, Kigali, Rwanda.
Sci Rep. 2025 Jul 2;15(1):22905. doi: 10.1038/s41598-025-06452-5.
Climate change exacerbates the challenges of maintaining crop health by influencing invasive pest and disease infestations, especially for cereal crops, leading to enormous yield losses. Consequently, innovative solutions are needed to monitor crop health from early development stages through harvesting. While various technologies, such as the Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI), have been used, portable, cost-effective, and energy-efficient solutions suitable for resource-constrained environments such as edge applications in agriculture are needed. This study presents the development of a portable smart IoT device that integrates a lightweight convolutional neural network (CNN), called Tiny-LiteNet, optimized for edge applications with built-in support of model explainability. The system consists of a high-definition camera for real-time plant image acquisition, a Raspberry-Pi 5 integrated with the Tiny-LiteNet model for edge processing, and a GSM/GPRS module for cloud communication. The experimental results demonstrated that Tiny-LiteNet achieved up to 98.6% accuracy, 98.4% F1-score, 98.2% Recall, 80 ms inference time, while maintaining a compact model size of 1.2 MB with 1.48 million parameters, outperforming traditional CNN architectures such as VGGNet-16, Inception, ResNet50, DenseNet121, MobileNetv2, and EfficientNetB0 in terms of efficiency and suitability for edge computing. Additionally, the low power consumption and user-friendly design of this smart device make it a practical tool for farmers, enabling real-time pest and disease detection, promoting sustainable agriculture, and enhancing food security.
气候变化通过影响有害生物和疾病的侵袭加剧了维持作物健康的挑战，尤其是对谷类作物而言，导致了巨大的产量损失。因此，需要创新的解决方案来监测作物从早期发育阶段到收获期的健康状况。虽然已经使用了各种技术，如物联网（IoT）、机器学习（ML）和人工智能（AI），但仍需要适用于资源受限环境（如农业边缘应用）的便携式、经济高效且节能的解决方案。本研究展示了一种便携式智能物联网设备的开发，该设备集成了一个名为Tiny-LiteNet的轻量级卷积神经网络（CNN），针对边缘应用进行了优化，并内置了模型可解释性支持。该系统由一个用于实时植物图像采集的高清摄像头、一个集成了Tiny-LiteNet模型用于边缘处理的Raspberry-Pi 5，以及一个用于云通信的GSM/GPRS模块组成。实验结果表明，Tiny-LiteNet的准确率高达98.6%，F1分数为98.4%，召回率为98.2%，推理时间为80毫秒，同时保持了1.2MB的紧凑模型大小和148万个参数，在效率和对边缘计算的适用性方面优于传统的CNN架构，如VGGNet-16、Inception、ResNet50、DenseNet121、MobileNetv2和EfficientNetB0。此外，这种智能设备的低功耗和用户友好设计使其成为农民的实用工具，能够实现病虫害的实时检测，促进可持续农业发展，并增强粮食安全。
