Recent IOT-based Expert Systems and Deep Learning Methods in Smart Farming

DOI

10.2991/978-94-6239-674-6_10How to use a DOI?

Keywords

IoT; Smart Farming; Deep Learning

Abstract

IoT (Internet of Things) has been widely used in the agriculture industry, ranging from food production and management to irrigation planning, as well as the prediction and prevention of diseases in plants and vegetables. This paper presents a review study on the IoT infrastructure, including its three-level components: sensors, communication networks, and server-side data storage, along with the associated software packages. Various machine learning (ML) algorithms such as ANN, ensemble learning, deep learning, and SVM have been deployed for diverse purposes, including disease prediction, classification, and management of agro-products like corn, fruits, vegetables, and other plants. Domain-specific programs for disease detection and management in certain fruits, corn, and vegetables are also described. Expert systems integrated with IoT and ML now offer real-time, edge-deployed solutions capable of early disease detection with minimal latency. Communication technologies such as LoRaWAN, NB-IoT, and hybrid LPWAN-5G networks have enhanced connectivity in remote farmlands. Sensor technologies have evolved to include optical, hyperspectral, acoustic, weather, and soil sensors, enabling comprehensive crop monitoring. A IoT architecture combines these sensors with expert decision layers and lightweight edge models. Modern ML approaches, including convolutional neural networks, vision transformers, ensemble methods like Random Forests, and federated learning, enhance disease classification and yield prediction while preserving data privacy. Autonomous platforms such as drones and rovers now assist in detection and treatment, forming closed loop “sense-predict-act” systems with integrated farm management dashboards and mobile applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Abhinav Arora
AU  - Adarsh Vardhan Srivastava
AU  - Chhote Lal Prasad Gupta
AU  - Shivakar Prasad
AU  - Ramesh Kumar Verma
PY  - 2026
DA  - 2026/05/28
TI  - Recent IOT-based Expert Systems and Deep Learning Methods in Smart Farming
BT  - Proceedings of the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI 2025)
PB  - Atlantis Press
SP  - 99
EP  - 112
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-674-6_10
DO  - 10.2991/978-94-6239-674-6_10
ID  - Arora2026
ER  -