Open AccessA Comprehensive Multi-Modal Framework for Plant Health Monitoring
Author(s) -
Mohammad Hanan Bhat
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36739
Subject(s) - cloud computing , computer science , gateway (web page) , point cloud , field (mathematics) , scope (computer science) , data collection , data science , structural health monitoring , identification (biology) , domain (mathematical analysis) , data mining , real time computing , artificial intelligence , engineering , world wide web , mathematical analysis , statistics , botany , mathematics , structural engineering , pure mathematics , biology , programming language , operating system
: Plant health monitoring has been a significant field of research since a very long time. The scope of this research work conducted lies in the vast domain of plant pathology with its applications extending in the field of agriculture production monitoring to forest health monitoring. It deals with the data collection techniques based on IOT, pre-processing and post-processing of Image dataset and identification of disease using deep learning model. Therefore, providing a multi-modal end-to-end approach for plant health monitoring. This paper reviews the various methods used for monitoring plant health remotely in a non-invasive manner. An end-to-end low cost framework has been proposed for monitoring plant health by using IOT based data collection methods and cloud computing for a single-point-of-contact for the data storage and processing. The cloud agent gateway connects the devices and collects the data from sensors to ensure a single source of truth. Further, the deep learning computational infrastructure provided by the public cloud infrastructure is exploited to train the image dataset and derive the plant health status