轻量视觉模型助力农民低成本识别柑橘病害
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings
- 用轻量化模型部署在边缘设备，适配资源受限环境
- Vision Transformer达96%柑橘品种分类准确率，YOLOv8-S检测效率高
- 适合农业现场应用，尤其适合发展中国家农户使用
本研究开发了一套轻量高效的计算机视觉流程，帮助农民在资源有限的条件下检测柑橘病害。系统整合了先进的目标检测、分类与分割模型，并针对边缘设备优化，确保在低算力环境下仍可运行。实验评估了多种前沿模型的性能，重点考察其准确性、计算效率和泛化能力。结果显示，Vision Transformer在柑橘品种分类上达到96%准确率，轻量版YOLOv8-S在目标检测任务中表现优异且计算开销极小。研究强调现代深度学习架构在解决农业关键问题中的潜力，突出模型复杂性与实际可用性之间的平衡。未来工作将拓展数据集、探索模型压缩技术及联邦学习，以提升系统在多样农业场景中的适用性，推动更可持续的农业生产。
原文摘要 · Abstract (English)
This research paper presents the development of a lightweight and efficient computer vision pipeline aimed at assisting farmers in detecting orange diseases using minimal resources. The proposed system integrates advanced object detection, classification, and segmentation models, optimized for deployment on edge devices, ensuring functionality in resource-limited environments. The study evaluates the performance of various state-of-the-art models, focusing on their accuracy, computational efficiency, and generalization capabilities. Notable findings include the Vision Transformer achieving 96 accuracy in orange species classification and the lightweight YOLOv8-S model demonstrating exceptional object detection performance with minimal computational overhead. The research highlights the potential of modern deep learning architectures to address critical agricultural challenges, emphasizing the importance of model complexity versus practical utility. Future work will explore expanding datasets, model compression techniques, and federated learning to enhance the applicability of these systems in diverse agricultural contexts, ultimately contributing to more sustainable farming practices.
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