用迁移学习对比多个模型，帮农村农民精准识别作物病害。
Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach
- 用EfficientNet、ResNet101等模型做迁移学习，提升小数据下的识别能力。
- 自研CNN模型在验证集上达到95.76%准确率，表现最优。
- 适合资源有限的农村地区，推动智能农业落地应用。
本研究开发了一套人工智能驱动的作物病害检测系统，旨在帮助资源匮乏的农村地区农民。通过对比多种深度学习模型在迁移学习中的表现，重点评估其在作物病害分类任务中的有效性。采用EfficientNet、ResNet101、MobileNetV2及自研CNN模型，其中自研CNN在验证集上达到95.76%的准确率，显著提升了病害识别性能。研究证明，迁移学习可有效改善作物健康管理，助力可持续农业在农村地区的推广与实践。
原文摘要 · Abstract (English)
This research presents the development of an Artificial Intelligence (AI) - driven crop disease detection system designed to assist farmers in rural areas with limited resources. We aim to compare different deep learning models for a comparative analysis, focusing on their efficacy in transfer learning. By leveraging deep learning models, including EfficientNet, ResNet101, MobileNetV2, and our custom CNN, which achieved a validation accuracy of 95.76%, the system effectively classifies plant diseases. This research demonstrates the potential of transfer learning in reshaping agricultural practices, improving crop health management, and supporting sustainable farming in rural environments.
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