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Distributed agricultural organization based on federated learning
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 2, Pages: 288-297(2022)
- 作者机构：
1. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
2. 中国科学院大学人工智能学院，北京 100049
3. 中国科学院自动化研究所北京市智能化技术与系统工程技术研究中心，北京 100190
4. 浙江省农业科学院数字农业研究所，浙江 杭州 310021
5. 宁波市农业技术推广总站，浙江 宁波 315012
6. 青岛中科慧农科技有限公司，山东 青岛 266000
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202231
CLC： S24-
Published Online：2022-06，
Published：15 June 2022
- 稿件说明：
移动端阅览
Mengzhen KANG, Xiujuan WANG, Dong LI, et al. Distributed agricultural organization based on federated learning[J]. Chinese journal of intelligent science and technology, 2022, 4(2): 288-297.
Mengzhen KANG, Xiujuan WANG, Dong LI, et al. Distributed agricultural organization based on federated learning[J]. Chinese journal of intelligent science and technology, 2022, 4(2): 288-297. DOI： 10.11959/j.issn.2096-6652.202231.
摘要
我国现阶段仍以小规模农业为主，如何在小农、小地块的农业生产经营方式下发展适宜的智慧农业颇具挑战。基于此，提出一种结合联邦学习与区块链技术的分布式农业人工智能框架，使得在数据不共享的情况下，能达到训练模型的目的，并可建立参与方的激励机制。该框架有助于充分利用农业多源异构数据，减少对用户数据量的要求，发展因地制宜的决策模型，促进小规模农业的产销衔接。
Abstract
At present
small-scale agriculture is dominating in China.How to develop appropriate smart agriculture for agricultural management with small farmers and small plots is quite challenging.A distributed agricultural AI framework combining federal learning and blockchain technology was proposed
which can achieve the purpose of the training model and establish the incentive mechanism for participants without data sharing.This framework helped make full use of agricultural multi-source heterogeneous data
reducing user data requirements
developing decision-making models according to local conditions
and promoting the connection of production and marketing of small-scale agriculture.
关键词
Keywords
references
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