Comparative Study of Machine Learning Models for Soil Fertilizer Classification in Precision Agriculture - Archevapanich, Tuanjai; Sirikham, Thanapat; Chaowalittawin, Vasutorn; Krungseanmuang, Woranidtha; Sathaporn, Posathip; Phumpho, Suwilai; Purahong, Boonchana (My University, 2025)
This study explores the machine learning techniques compare for fertilizer classification based on soil nutrient dataset aligning with the goals of precision agriculture. Five models include Random Forest, Logistic Regression, SVM, XGBoost and Neural Network(ANN) were tested using precision, accuracy, F1-score, recall and confusion matrices. The highest F1-score is XGBoost model, while the best precision performance delivered by Random Forest model. Results emphasize the significance of model selection in handling imbalanced agricultural data. The approach supports data-driven decision-making for sustainable farming aligned with Thailand's 20-Year Agricultural Strategic Plan.
Keywords: Agricultural innovation; Comparison study; Fertilizer Classification; Machine Learning; Precision Agriculture