An LED-based multispectral discrete spectrometer for in-situ soil moisture sensing using machine learning algorithms
Computers and Electronics in Agriculture, cilt.250, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 250
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.compag.2026.111948
- Dergi Adı: Computers and Electronics in Agriculture
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, BIOSIS, Compendex, Environment Index, Geobase, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Anahtar Kelimeler: In-situ sensing, LED-based sensors, Machine learning, Near-infrared spectroscopy, Precision agriculture, Soil moisture content, Soil spectroscopy
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Accurate, low-cost, and field-deployable soil moisture content (SMC) sensing remains a key challenge in precision agriculture and environmental monitoring. This study presents a compact and energy-efficient approach for in-situ SMC estimation using discrete near-infrared (NIR) spectroscopy combined with machine-learning (ML) algorithms. For this purpose, a portable transfer-standard sensor was developed using seven selected NIR light-emitting diodes (LEDs), targeting strong water absorption bands (970, 1150, 1450, and 1900 nm) and weak-absorption reference wavelengths (1100, 1300, and 1650 nm) for soil reflectance measurements. The optomechanical design complies with international soil reflectance spectroscopy standards, employing nadir illumination and 45° detection geometry, while enabling direct comparability between laboratory and field measurements. SI-traceable calibration was performed using thermogravimetric methods over an SMC range from dry conditions to approximately 25% below saturation, utilizing more than ten soil types with varying composition and texture. Linear physical models based on relative absorption depth showed good performance under soil-specific calibration but degraded under heterogeneous field conditions, highlighting sensitivity to soil texture and composition properties. To address this limitation, six ML regression models were evaluated using stratified K-fold cross-validation, feature selection, and normalization. All ML models outperformed the physical approach, with Gaussian Process Regression achieving the highest accuracy, yielding errors below 0.5% for soil-specific calibration and below 2% under generalized field conditions, in agreement with gravimetric references. Wavelength subset analysis revealed diminishing returns beyond five LEDs, with a combination of strong and weak absorption bands providing the most informative feature set. Overall, the results demonstrate that LED-based discrete NIR spectroscopy integrated with ML offers a robust, scalable, and cost- and energy-efficient alternative to conventional spectroscopic systems for in-situ soil moisture monitoring.