Afan Ghafar Al Hadad, Rachmad Setiawan, Totok Mujiono, Abdul Latief Abadi, Yohana Avelia Sandy, Firdausa Sonna Anggara Resta, Rezki El Arif
Basal Stem Rot (BSR) caused by Ganoderma boninense is a primary threat to oil palm productivity and is difficult to detect early because visual symptoms appear late. This study presents a detection and severity-classification framework for healthy, mild, and severe based on dielectric characteristics measured ex vivo with a Vector Network Analyzer (VNA) and a Dielectric Assessment Kit (DAK) open-ended coaxial probe over 4 MHz-3 GHz with open-short-load (OSL) calibration. Four dielectric parameters, namely dielectric constant (Er ′), dielectric loss factor Er ″, loss tangent (tan δ), and conductivity (σ), were summarized into 20 features per sample using simple statistics, then learned by three Extreme Gradient Boosting (XGBoost) classifiers specialized per plant part: leaf, frond, and root. An all parts combined model was also evaluated as an additional baseline. A balanced dataset of 1,080 samples with 360 samples per plant part was collected at Pusat Penelitian Kelapa Sawit (PPKS) Marihat. Models were trained with an 80% training and 20% test split, using stratified five-fold cross-validation on the training set for hyperparameter tuning. On the held-out test data, macro-F1 reached 0.903 for leaf, 0.917 for frond, 0.931 for root, and 0.939 for the combined model, with errors concentrated at adjacent class boundaries. These results show that VNA-based dielectric measurement combined with simple feature engineering and per-part models can detect and classify BSR severity accurately and reproducibly, providing an ex vivo proof-of-concept baseline to guide future field validation and development toward more practical measurement approaches. © 2013 IEEE.
Institut Teknologi Sepuluh Nopember, Department of Electrical Engineering, Surabaya, 60111, Indonesia; Institut Teknologi Sepuluh Nopember, Department of Biomedical Engineering, Surabaya, 60111, Indonesia; Universitas Brawijaya, Department of Plant Pests and Diseases, Malang, 65145, Indonesia
