Abstract
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.
Abstract (translated)
这项研究开发了一个由人工智能（AI）驱动的农作物病害检测系统，旨在帮助资源有限的农村地区农民。我们旨在比较不同的深度学习模型进行对比分析，重点关注它们在迁移学习中的有效性。通过利用包括EfficientNet、ResNet101、MobileNetV2和我们的自定义CNN在内的深度学习模型（后者实现了95.76%的有效验证准确率），该系统能够有效地对植物病害进行分类。本研究展示了迁移学习在重塑农业实践、改善作物健康管理以及支持农村环境可持续耕作方面的潜力。
URL
https://arxiv.org/abs/2506.20323
