Abstract :

The Analysis Of Plant Growth And Development In Controlled Agricultural Environments Has Gained Importance Due To The Increasing Demand For Sustainable And Efficient Food Production. Traditional Plant Monitoring Methods Relied On Manual Observation And Basic Statistical Techniques, Which Offered Limited Understanding Of Complex Environmental Interactions. With Advancements In Sensor Technologies And Smart Farming Systems, Large Volumes Of Environmental And Plant-related Data Are Continuously Generated. However, Conventional Analytical Approaches Struggle To Process High-dimensional Datasets And Often Fail To Accurately Predict Plant Development Patterns. To Address These Challenges, This Study Proposes A Machine Learning (ML)-based Analytical Framework That Integrates Data Preprocessing, Exploratory Data Analysis (EDA), Classification Models, Regression Models, And Hybrid Deep Learning Techniques For Plant Development Analysis. Classification Algorithms Such As Support Vector Classifier (SVC), Bernoulli Naive Bayes Classifier (BNC), And Multinomial Naive Bayes Classifier (MNC) Are Employed To Identify Plant Development Stages. Regression Models Including Decision Tree Regressor (DTR), Support Vector Regressor (SVR), And Ridge Regressor (RR) Are Utilized To Predict Plant Growth Parameters. Furthermore, Two Hybrid Models Are Introduced To Enhance Predictive Performance: Feed Forward Neural Network (FFNN) Combined With Gaussian Naive Bayes (GNB), Termed Deep Feature Probabilistic Classifier (DFPC), And FFNN Integrated With RR, Referred To As Hybrid Deep Ridge Predictor (HDRP). Experimental Results Demonstrate That DFPC Achieves A Classification Accuracy Of 94.42%, While HDRP Attains A Highly Accurate Prediction Performance With An R² Score Of 0.999. These Findings Highlight The Effectiveness Of Combining Deep Feature Extraction With Machine Learning Techniques For Precise Plant Development Analysis And Improved Decision-making In Controlled Agricultural Systems.

Published:

24-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

815-825

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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