Energy-Efficient Rainfall Prediction Using Support Vector Machine on Edge Ai Platforms

Willy Permana Putra - Politeknik Negeri Indramayu, Indramayu, 45252, Indonesia
Robi Robiyanto - Politeknik Negeri Indramayu, Indramayu, 45252, Indonesia
Alifia Puspaningrum - Politeknik Negeri Indramayu, Indramayu, 45252, Indonesia
Ahmad Rifai - Politeknik Negeri Indramayu, Indramayu, 45252, Indonesia
Renol Burjulius - Politeknik Negeri Indramayu, Indramayu, 45252, Indonesia
A Sumarudin - Politeknik Negeri Indramayu, Indramayu, 45252, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3-2.3158

Abstract


The integration of AI into various sectors, including agriculture, has been advancing significantly. Implementing AI in the context of IoT and edge AI presents challenges due to resource limitations. Current climate changes affect planting strategies, pest management, and harvest timing. This study explores an SVM-based machine-learning model with multiple kernels to classify weather conditions as rainy or clear. The research includes two phases: model training on a PC-based system and model deployment on an edge AI device. The training phase includes preprocessing with PCA and fine-tuning of parameters, such as kernel types (linear, polynomial, sigmoid, and RBF), C and gamma. The development phase involves deploying the model on an ESP32, where execution time and power consumption are evaluated. The results show that the SVM model with an RBF kernel, C of 0.1, and gamma of 1 achieves a precision of 79.37%. Inference on the ESP32 yields an average execution time of 35.5 ms and a power consumption of 66 mA, showing a 202-fold reduction in power usage compared to the PC-based system and a 59-fold increase in execution time. This reduced power consumption supports the feasibility of edge AI for climate-based agricultural applications, enabling effective rainfall prediction. The findings contribute to the development of precision agriculture by providing insights into climate prediction, which can inform planting decisions, pest management, and harvest timing, thereby advancing the application of edge AI in response to global climate change.

Keywords


edge AI; SVM; Energy-efficient; rainfall prediction; agriculture;

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References


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