从水稻病害识别出发探索农业数据共享新模式

A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

准确高效地识别作物病害类型,有助于农户及时采取有效的针对性预防措施,从而降低因病虫害导致的减产风险和经济损失。然而,在其他领域能达到SOTA效果的识别模型,在农业领域特别是水稻病害识别的应用中,却面临目前已有的水稻病害数据量不足、种类不丰富以及数据质量不高等问题。本研究采用多种经典卷积神经网络,并利用迁移学习的方法在两个不同的数据集上进行训练。验证了除模型结构带来的优化外,训练数据集本身对于训练结果也具有重要影响。但目前农业领域开源数据较少,几乎没有综合性的数据开源平台可供利用。这一现象与高质量农业数据获取难度大且成本高、大多数从业人员教育水平相对较低、分布式训练系统不成熟、数据安全问题得不到保障等因素密切相关。针对农业领域训练中高质量数据缺乏的问题,在本文中提出了基于联邦学习框架构建农业数据共享平台的新思路。

Accurate and efficient identification of crop diseases can enable farmers to take effective and targeted preventive measures in a timely manner,which is helpful to reduce the risk of yield reductions and economic losses caused by crop diseases.However,the recognition model that can achieve the effect of SOTA in other fields,especially in the application of rice disease identification,faces the challenge of insufficient available rice disease data,a limited range of disease varieties and low data quality.In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning methods.We demonstrated that in addition to the optimization achieved through model structure,the training data set itself has an important impact on the training results.However,the scarcity of open-source agricultural data,coupled with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To solve those challenges,this paper proposed a novel idea to construct an agricultural data sharing platform based on federated learning framework,aiming to address the deficiency of high-quality data in agricultural field training.