Federated Learning Based Smart Horticulture and Smart Storage of Fruits Using E-Nose, and Blockchain: A Proposed Model

Shakhmaran Seilov, Bishwajeet Pandey, Akniyet Nurzhaubayev, Dias Abildinov, Assem Konyrkhanova, Bibinur Zhursinbek
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Abstract

The main objective of this project is to increase the productivity of farmers producing fruits and vegetables in Kazakhstan. We are planning to use technology during production at orchards and also using technology during storage. At the production stage, we shall capture images of fruit flowers, growing fruits, and a ripe fruit. Then we shall apply federated learning to train our model with healthy fruits and flowers and then we shall be able to predict any ongoing pest infections with either fruits or flower. At the storage phase, we shall use e-nose to check the current status of apple and save it from any possible degradation. We shall also use blockchain to store data related to fruits at both stages of production and storage to create an e-passport that will give access to data related to production and storage of fruits. At the same time, we shall also use various width clustering algorithms to detect intrusion in our sensor based IoT networks.
使用电子鼻和区块链的基于联合学习的智能园艺和水果智能存储:一个拟议模型
该项目的主要目标是提高哈萨克斯坦水果和蔬菜生产农民的生产率。我们计划在果园生产过程中使用技术,并在储存过程中使用技术。在生产阶段,我们将捕捉果花、生长中的果实和成熟果实的图像。然后,我们将应用联合学习,用健康的果实和花朵来训练我们的模型,这样我们就能预测果实或花朵是否正在受到害虫感染。在储存阶段,我们将使用电子鼻来检查苹果的当前状态,并将其从任何可能的退化中保存下来。我们还将使用区块链来存储水果在生产和贮藏两个阶段的相关数据,以创建一个电子护照,用于访问水果生产和贮藏的相关数据。同时,我们还将使用各种宽度聚类算法来检测基于传感器的物联网网络中的入侵行为。
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