Title: Deep learning and IoT for detecting and classifying leaf diseases in Agriculture 4.0: a systematic review

Authors: Swarna Prabha Jena; Sujata Chakravarty; Bijay Kumar Paikaray

Addresses: Department of ECE, Centurion University of Technology and Management, Odisha, India ' Department of CSE, Centurion University of Technology and Management, Odisha, India ' Center for Data Science, SOA University, Odisha, India

Abstract: Advancement of digital technology in agriculture leads to Agriculture 4.0. The technological revolution has provided the detection and classification of leaf diseases at an early stage. Hence, advancements are essential to reduce costs and increase quality and productivity. Deep learning techniques empower the system's development, allowing better decisions to be made early, saving time and money. Nowadays, incorporating IoT in agriculture makes the system intelligent enough to support farmers independently. To know the direction, numerous methods have been systematically reviewed and kept in place to make the researcher understand. Learning the current state-of-the-art techniques for hardware requirements, dataset used, and performance matrices is crucial. Therefore, the core objective of the research article is to do a systematic literature review that analyses the research gaps, current trends, challenges and answers the research questions, which are helpful for investigation. Finally, this systematic review can be the starting point for upcoming researchers.

Keywords: systematic review; plant diseases; deep learning; embedded platform; internet of things; IoT.

DOI: 10.1504/IJIMS.2024.142540

International Journal of Internet Manufacturing and Services, 2024 Vol.10 No.4, pp.346 - 376

Received: 01 Sep 2023
Accepted: 05 Nov 2023
Published online: 08 Nov 2024 *

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