Abstract: Agriculture has a considerable contribution to the economy. Agriculture automation is a serious issue that is becoming more prevalent around the world. Farmers' traditional practices were insufficient to achieve these objectives. Artificial Intelligence (A1) and the Internet of Things (IoTs) are being used in agriculture to improve crop yield and quality. Distributed solar energy resources can now be remotely operated, monitored, and controlled through the IoT and deep learning technology. The development of an IoT-based solar energy system for intelligent irrigation is critical for water- and energy-stressed areas around the world. The qualitative design focuses on secondary data collection techniques. The deep learning model Radial Basis Function Networks (RBFN) is used in conjunction with the Elephant Search Algorithm (ESA) in this IoT-based solar energy system for future smart agriculture. Sensor systems help farmers understand their crops better, reduce their environmental impact and conserve resources. These advanced systems enable effective soil and weather monitoring, as well as water management. To provide the required operating power, the proposed system, RBFN-ESA, employs an IoT-based solar cell forecasting process. The proposed model RBFN-ESA will collect these data to predict the required parameter values for solar energy systems in future smart agriculture systems. The results of the RBFN-ESA model are effective and efficient. According to the findings, RBFN-ESA outperforms CNN, ANN, SVM, RF, and LSTM in terms of energy consumption (56.764J for 100 data points from the dataset), accuracy achieved (97.467% for 600 nodes), and soil moisture level (94.41% for 600 data).

Vidya M S, Ravi Kumar B. N, Anil G. N and Ambika G. N. “Deep Learning with IoT-Based Solar Energy System for Future Smart Agriculture System”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.9 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150944

@article{S2024,
title = {Deep Learning with IoT-Based Solar Energy System for Future Smart Agriculture System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150944},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150944},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Vidya M S and Ravi Kumar B. N and Anil G. N and Ambika G. N}
}