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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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Climate-Aware Crop Recommendation Using IoT And Deep Ensemble Learning: A Case Study in Semi-Arid Agro-Ecological Zones

Gaurav Pandey
Assistant Professor, Department of Applied Science, FET, Rama University, India.

Keywords: Climate-aware agriculture, Crop recommendation, Deep ensemble learning, IoT in farming, Semi-arid zones.

Abstract

Rainfed agricultural productivity in semi-arid areas is under severe threat primarily because of low and erratic rainfall and high temperature under resource poor situations. Current crop recommendation systems do not always account for the dynamic relationships among variation in climate, soil heterogeneity, and local crop suitability particularly in fragile crop ecosystems. In this paper, we present a climate-smart crop recommendation system using the data sensed by Internet of Things (IoT) sensors along with the deep ensemble learning to recommend the right crops at the right places and the right time with high accuracy. The platform combines profiles of real-time soil moisture, pH and electrical conductivity as well as weather information for the day and forecasted with historical climate patterns to present personalized crop prescriptions that are tuned for drier, semi-arid conditions. As a result of leveraging the strengths of multiple deep learning models (CNN, LSTM, XGBoost), the ensemble method is superior in terms of prediction stability and accuracy when compared with single-model configurations. Uniquely, unlike previous models that provide general guidelines, we provide detailed advice by considering different millet varieties, the best sowing times, and irrigation practices, etc. Engineered for affordable access, with a small ecological footprint, the model provides local farmers with eco-friendly, climate-resilient croplands. Experimental results show that the recommended accuracy and ecological adaptability are greatly improved, which suggests the potential for the proposed system in large-scale intelligent agriculture in climate-stressed area.
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Published
2025-06-26
Pages
1-14
Issue
Vol. 4 No. 2 (2025): IJRTTE - 04 - 02
Section
Articles

References

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