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Energy efficient quantum-inspired IoT-based crop recommendation framework for smart agriculture
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DOI：10.1016/j.jpdc.2026.105267.png)
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Abstract
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India is a country where farming has been known to be practised for ages. To be able to produce food on its own has surely contributed significantly to the growth of nation. The agriculture yield can be increased by analyzing the soil properties and recommending the right crop for that area. This paper proposed an energy efficient fog-cloud assisted crop recommendation system to serve this purpose. The data of various soil attributes is collected using sensors. Selective data is forwarded to the Cloud layer by analyzing the spatio-temporal correlation among the collected data. The soil profile analysis is performed at fog layer using soil features to identify the type of soil. At cloud layer, the values of weather data are forecasted using Quantum LSTM (QLSTM) and crop recommendation analysis is performed using deep neural network using weather and soil data. Experimentation and Performance analysis demonstrates the effectiveness of the proposed system. Moreover, the energy conservation analysis of the proposed architectural framework is carried out to prove the efficiency of spatio-temporal data correlation based data selection technique. .
Keywords:
Crop recommendation
Machine learning
Quantum computing
SMOTE
Energy efficiency
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