Feature-Driven and Ensemble Learning Models for Predicting Plant Communication Signals and Smart Farming Recommendations
DOI:
https://doi.org/10.31838/NJAP/08.02.19Keywords:
Machine Learning, Plant Communication Signals, Physiological, XGBoost, Support Vector Machine (SVM)Abstract
This research aims to present feature-driven ensemble learning framework for predicting plant communication signals and delivering intelligent farming recommendations using five Machine Learning algorithms (ML) such as Random Forest (RF), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbours (KNN) and Logistic Regression (LR) were applied to a structure Plant Communication Dataset consisting of physiological and environmental features such as leaf vibration, bioluminescence intensity, and soil moisture level. The goal is to classify the plant messages into four different ways: Warning, Contentment, Distress and Invitation. To prove the work of the model’s confusion matrices and classification metrics (Accuracy, Precision, Recall, F1-Score) were used and feature importance score used to explain the influence of root signals and soil moisture to predict the behaviour of the plant. The RF algorithm achieved the highest accuracy of 99.2 %, outperforming other machine learning methods. The proposed ensemble-based framework offers a transparent and accurate solution for real-time plant behaviour prediction and supports smart agriculture systems through plant signaling analysis.
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