Current Volume 9
Abstract
Agriculture plays a major role in economic development and global food security. However, challenges such as pest infestation, drought conditions, crop diseases, and irregular climate patterns significantly affect agricultural productivity and farmer income. Traditional agricultural monitoring methods mainly rely on manual inspection, historical observations, and farmer experience, which are often time-consuming, laborintensive, less accurate, and inefficient for early-stage detection. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Internet of Things (IoT), Remote Sensing, and Explainable AI (XAI) technologies have created new opportunities for intelligent agricultural monitoring and prediction systems [14], [15]. These technologies can automate crop monitoring, provide early warnings, improve decisionmaking, and support sustainable farming practices. This paper proposes a conceptual AI-based smart agricultural monitoring and prediction framework that integrates Deep Learning models, IoT-enabled sensors, satellite imagery, weather analysis, and Explainable AI techniques for real-time pest detection and drought prediction. The framework includes modules for image acquisition, preprocessing, feature extraction, pest classification, drought prediction, explainable prediction analysis, and farmer alert generation. Deep learning models such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Random Forest, and LSTM models are proposed for intelligent agricultural analysis. The proposed system aims to improve crop productivity, reduce crop losses, optimize water management, reduce pesticide overuse, and support precision agriculture. Implementation and experimental validation are planned as future work.
Keywords
Artificial Intelligence, Smart Agriculture, Pest Detection, Drought Prediction, Deep Learning, CNN, IoT, Explainable AI, Machine Learning, Remote Sensing, Precision Agriculture
Citations
IRE Journals:
Lakshmi Priya G, Dr. Ganesh D "Smart Agriculture System for Pest Detection and Drought Prediction using AI" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2899-2904 https://doi.org/10.64388/IREV9I11-1717986
IEEE:
Lakshmi Priya G, Dr. Ganesh D
"Smart Agriculture System for Pest Detection and Drought Prediction using AI" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717986
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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