Yerinde Problar Olmadan Etkili Toprak Nemi İzleme: LSTM Tabanlı Bluetooth Sinyal Gücü Analizi

Year 2024, Volume: 25 Issue: 1, 21 - 38

Selçuk Yazar , Deniz Taşkın , Erdem Bahar

https://doi.org/10.59314/tujes.1464575

Abstract

Toprak yapısına zarar vermeden toprak nemi ölçümü tarımda önemlidir. Elektriksel iletkenlik ve mikrodalgalar bu amaçla yaygın olarak kullanılmaktadır. Son zamanlarda, toprak nem içeriğini belirlemek için yapay sinir ağları ve zaman serisi tahminlerinin kullanılmasına olan ilgi artmaktadır. Bu ölçümlerde mikrodalga cihazlara benzer şekilde Bluetooth sinyallerinden yararlanılmaktadır. Ancak Bluetooth sinyalleri, özel toprak nemi ölçüm cihazlarına kıyasla düşük iletim gücüne sahiptir. Bu çalışmada, Uzun Kısa Süreli Bellek (LSTM) sinir ağı mimarisi ve 0,001 Watt iletim gücüne sahip Bluetooth sinyal güçleri kullanılarak, özellikle farklı pH değerlerine sahip toprak örnekleri için toprak nem içeriğinin belirlenme olasılığı araştırılmaktadır. Amaç, toprak nem değişim durumunu yerinde bir prob olmadan doğrudan Bluetooth sinyal seviyelerini kullanarak değerlendirmekti. Deneysel bir çalışmada, alkali toprak örneklerinden elde edilen Bluetooth sinyal güçlerine dayalı bir yapay öğrenme modeli kullanılarak toprak nem içeriği değişimi %15'lik bir kök-ortalama-kare hata (RMSE) değeri ile tahmin edilmiştir. Bu yöntem, toprak nemi zaman içindeki sinyal seviyesi değişiklikleri izlenerek güvenilir bir şekilde ölçülebildiğinden, özel bir sensör ihtiyacını ortadan kaldırmaktadır.

Keywords

Bluetooth Düşük Enerji, Derin öğrenme, Uzun-Kısa Süreli Bellek, Aktif mikrodalgalar, Toprak nemi

Efficient Soil Moisture Monitoring without In-Situ Probes: LSTM-Based Bluetooth Signal Strengths Analysis

Abstract

Soil moisture measurement without damaging soil structure is important in agriculture. Electrical conductivity and microwaves are commonly used for this purpose. Recently, there has been growing interest in using artificial neural networks and time series forecasting to determine soil moisture content. Bluetooth signals, similar to microwave devices, are utilized in these measurements. However, Bluetooth signals have low transmission power compared to dedicated soil moisture measurement devices. This study investigates the possibility of determining soil moisture content using Long Short-Term Memory (LSTM) neural network architecture and Bluetooth signal strengths with 0.001 Watt transmission power, specifically for soil samples with varying pH values. The objective was to assess soil moisture change status directly using Bluetooth signal levels without an in-situ probe. In an experimental study, soil moisture content changing was predicted with a root-mean-square error (RMSE) value of 15% using an artificial learning model based on Bluetooth signal strengths obtained from alkali soil samples. This method eliminates the need for a dedicated sensor, as soil moisture can be reliably measured by tracking signal level changes over time.

Keywords

Bluetooth Low Energy, Deep learning, Long Short-Term Memory, Active microwaves, Soil moisture

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