Deep Learning-Enhanced Dielectric Sensing for Rapid Quality Assessment of 'Starks Gold' Sweet Cherries
AGRONOMY-BASEL, cilt.16, sa.12, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 12
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/agronomy16121161
- Dergi Adı: AGRONOMY-BASEL
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Geobase, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest)
- Ankara Üniversitesi Adresli: Evet
Özet
Soluble solids content (SSC) is one of the most important indicators of sweetness, ripeness, and market quality in sweet cherries. However, conventional SSC determination is destructive, labor-intensive, and unsuitable for rapid or large-scale quality assessment. Therefore, there is a need for fast, non-destructive, and data-driven sensing approaches that can estimate internal fruit quality without damaging the sample. This study aimed to develop a non-destructive approach for SSC prediction in sweet cherries by combining open-ended coaxial probe dielectric spectroscopy with deep learning models. An open-ended coaxial probe measurement system was designed and developed to determine the dielectric properties of sweet cherries and was coupled with an Agilent E4991A impedance analyzer operating over a frequency range of 5-3005 MHz. A total of 10,080 dielectric measurements and 2100 reference SSC measurements were collected over 26 experimental days. The dielectric constant (epsilon '), loss factor (epsilon ''), and loss tangent (tan delta) were extracted and used to construct separate epsilon ', epsilon '', tan delta, and integrated combined datasets. Six deep learning architectures, namely convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), CNN-LSTM, and convolutional long short-term memory (ConvLSTM), were trained and optimized using Bayesian optimization and early stopping. CNN achieved the best performance on the tan delta dataset (test R2 = 0.9099, RMSE = 0.8354 degrees Brix, MAE = 0.6599 degrees Brix), whereas GRU yielded the highest accuracy on the integrated combined dataset (test R2 = 0.8622, RMSE = 1.0331 degrees Brix, MAE = 0.7958 degrees Brix). ConvLSTM provided the most consistent performance across all four datasets (test R2 = 0.8081-0.8651), demonstrating strong predictive capability and practical computational efficiency. These findings confirm the potential of reduced-range dielectric spectroscopy combined with deep learning for rapid, non-destructive SSC assessment in sweet cherries.