Ingeniería SolidariaResearch article. https://doi.org/10.16925/2357-6014.2023.03.03 1 Lovely Professional University, India Email: bhuvanpuri1199@gmail.com ORCID: https://orcid.org/0000-0002-3098-7892 2 Lovely Professional University, India Email: rameshwar.20345@lpu.co.in ORCID: https://orcid.org/0000-0002-5369-7433 A review on the role of IoT, ai, and blockchain in agriculture & crop diseases detection using a text mining approach
Introduction: This paper is the outcome of a review survey, “Role of IoT, AI and blockchain in agriculture and crop disease detection using a text mining approach,” done at Lovely Professional University in Punjab, India, in 2023. Problem: Agriculture is a crucial industry that contributes significa...
Saved in:
Published in: | Revista ingeniería solidaria Vol. 19; no. 3; pp. 1 - 30 |
---|---|
Main Authors: | , |
Format: | Journal Article |
Language: | English |
Published: |
15-09-2023
|
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | Introduction: This paper is the outcome of a review survey, “Role of IoT, AI and blockchain in agriculture and crop disease detection using a text mining approach,” done at Lovely Professional University in Punjab, India, in 2023. Problem: Agriculture is a crucial industry that contributes significantly to the economies of many nations. Crop diseases are one of the issues that create a barrier to agricultural development. Objective: Using machine learning, deep learning, image processing methods, the Internet of Things, and blockchain technology, this study provides a current summary of research done over the past years on disease identfication of various crops. Methodology: The text mining technique is applied to extract the related information from published papers and predict the following futuristic technologies to detect crop diseases early. Results: This paper also covers the various issues, challenges, and potential solutions. It also emphasizes the wide range of tools available for identifying crop diseases. Conclusion: This paper helps to extract valuable keywords through a text-mining approach and create a roadmap for another researcher. Originality: Applied text mining visualization techniques, such as word cloud and word frequency, to extract the keywords. Limitation: The literature survey only covers literature published prior to February 2023; it can be extended with more studies published soon. |
---|---|
ISSN: | 2357-6014 2357-6014 |
DOI: | 10.16925/2357-6014.2023.03.03 |