Predicting Soil Moisture Levels with Gated Recurrent Units: A Deep Learning Approach Integrated with Internet of Things Data - Dalam bentuk buku karya ilmiah

FAHMI ROZAN MUSTAFA

Informasi Umum

Kode

25.04.1321

Klasifikasi

000 - General Works

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Deep Learning

Dilihat

42 kali

Informasi Lainnya

Abstraksi

This study examines the prediction of soil moisture levels using Gated Recurrent Units (GRU) integrated with Internet of Things (IoT) technology. Recognizing the critical role of soil moisture in plant growth and ecosystem health, the research employs realtime data collected from IoT devices, including soil moisture, temperature, and humidity, to enhance irrigation efficiency in agricultural practices. Various prediction samples (nsamples) were analyzed, specifically focusing on nsamples of 6, 12, 18, and 24. The findings reveal that the GRU model with a prediction sample of n_samples =12 achieved the highest accuracy, bring in an R² value of 0.89944 and the lowest Mean Absolute Percentage Error (MAPE) of 0.03201. In contrast, shorter and longer prediction intervals demonstrated decreased accuracy and increased error rates. The study underscores the importance of selecting optimal prediction intervals for reliable soil moisture forecasting and highlights the potential of GRU models in real-time environmental monitoring. By combining deep learning methodologies and IoT technology, this research contributes to more efficient irrigation practices that can enhance water conservation and improve crop yields, ultimately promoting sustainable agricultural management strategies. Future work may focus on further enhancing model performance and expanding its applicability across diverse agricultural contexts.