Author:
Kaplun Dmitrii,Deka Surajit,Bora Arunabh,Choudhury Nupur,Basistha Jyotishman,Purkayastha Bhaswadeep,Mazumder Ifthikaruz Zaman,Gulvanskii Vyacheslav,Sarma Kandarpa Kumar,Misra Debashis Dev
Abstract
AbstractContrary to popular belief, agriculture is becoming more data-driven with artificial intelligence and Internet-of-Things (IoT) playing crucial roles. In this paper, the integrated processing executed by various sensors combined as an IoT pack and driving an intelligent agriculture management system designed for rainfall prediction and fruit health monitoring have been included. The proposed system based on an AI aided model makes use of a Convolutional Neural Network (CNN) with long short-term memory (LSTM) layer for rainfall prediction and a CNN with SoftMax layer along with a few deep learning pre-trained models for fruit health monitoring. Another model that works as a combined rainfall predictor and fruit health recognizer is designed using a CNN + LSTM and a multi-head self-attention mechanism which proves to be effective. The entire system is cloud resident and available for use through an application.
Funder
Ministry of Science and Higher Education of the Russian Federation
Publisher
Springer Science and Business Media LLC