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Context-Aware Smart Agriculture Using AI and IoT Sensing Use Deep Learning Models

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Context-Aware Smart Agriculture Using AI and IoT Sensing Use Deep Learning Models


Mr. Saurav Kumar | Mr. Chetan Kumar



Mr. Saurav Kumar | Mr. Chetan Kumar "Context-Aware Smart Agriculture Using AI and IoT Sensing Use Deep Learning Models" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-10 | Issue-3, June 2026, pp.422-425, URL: https://www.ijtsrd.com/papers/ijtsrd102066.pdf

This paper presents an intelligent, context-aware smart agricultural framework that utilizes deep learning architectures and Internet of Things (IoT) multi-sensor data fusion to optimize precision irrigation and predict crop disease [1]. Traditional automated farming relies on static, single-variable threshold triggers that fail to adapt to dynamic microclimate fluctuations, leading to water wastage and delayed pathogen detection [1]. To resolve these inefficiencies, we deploy a decentralized edge-fog-cloud infrastructure that continuously ingests heterogeneous environmental telemetry, including ambient temperature, relative humidity, soil volumetric water content, and solar irradiance [1]. This real-time data stream is processed by a dual-engine deep learning pipeline: a Long Short-Term Memory (LSTM) network engineered to predict dynamic soil moisture depletion curves for context-aware irrigation scheduling, and a lightweight Convolutional Neural Network (CNN) deployed on edge nodes for localized, early-stage foliar disease identification. Experimental results indicate that our context-aware framework reduces irrigation water consumption by 34% while maintaining optimum soil moisture tension and achieves a 94.2% classification accuracy in identifying crop pathogens up to five days before visible macroscopic symptoms manifest. Ultimately, this integrated architecture offers a highly scalable, resource-efficient, and cyber-secure solution for modern sustainable farming and climate-smart agriculture [1].

Context-Aware Agriculture, Internet of Things (IoT), Deep Learning, Long Short-Term Memory (LSTM), Precision Irrigation, Crop Disease Prediction [1].


IJTSRD102066
Volume-10 | Issue-3, June 2026
422-425
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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