基于深度学习与社交媒体的植物病害智能诊断系统
Advanced Machine Learning Framework for Efficient Plant Disease Prediction
- 利用深度学习从植物图像中识别病害
- 结合社区反馈和概念漂移检测优化解决方案推荐
- 适合作为农民互助平台的技术参考
近年来，机器学习方法已成为众多智慧农业平台的重要组成部分。本文提出一种新型机器学习框架，构建智慧农业平台，使农户可向公众或专家圈寻求帮助。重点在于为农户提供一种简便方式理解植物病害，通过社区成员协助解决实际问题。该系统首先利用深度学习技术从受病害影响的图像中识别病害，作为初步诊断工具；随后采用自然语言处理技术对用户社区发布的解决方案进行排序。本文在推特（Twitter）平台上构建消息通道，实现农户间的有效沟通。由于解决方案的效果会随多种参数变化，我们引入概念漂移方法，动态调整推荐策略并给出最优建议。在基准数据集上的测试表明，该框架能产生准确可靠的预测结果。
原文摘要 · Abstract (English)
Recently, Machine Learning (ML) methods are built-in as an important component in many smart agriculture platforms. In this paper, we explore the new combination of advanced ML methods for creating a smart agriculture platform where farmers could reach out for assistance from the public, or a closed circle of experts. Specifically, we focus on an easy way to assist the farmers in understanding plant diseases where the farmers can get help to solve the issues from the members of the community. The proposed system utilizes deep learning techniques for identifying the disease of the plant from the affected image, which acts as an initial identifier. Further, Natural Language Processing techniques are employed for ranking the solutions posted by the user community. In this paper, a message channel is built on top of Twitter, a popular social media platform to establish proper communication among farmers. Since the effect of the solutions can differ based on various other parameters, we extend the use of the concept drift approach and come up with a good solution and propose it to the farmer. We tested the proposed framework on the benchmark dataset, and it produces accurate and reliable results.
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