Hyperparameter-Tuned Convolutional Neural Network Models for Unmanned Aerial Vehicle-Assisted Plant Pathology Monitoring

This study addresses the challenges of hyperparameter optimization in Convolutional Neural Networks (CNNs) for crop health classification under simulated UAV-based monitoring conditions using a publicly available multimodal dataset of laboratory-captured leaf images and environmental sensor readings. While CNNs have proven effective in agricultural image analysis, their performance is highly sensitive to hyperparameter configurations such as learning rate, batch size, and network architecture, particularly in multimodal, end-to-end training scenarios. Traditional methods, such as grid search and random search, are computationally inefficient for high-dimensional agricultural data. To overcome these limitations, this study implements advanced optimization techniques, including Bayesian optimization and Particle Swarm Optimization (PSO), to enhance CNN performance in terms of classification accuracy, training convergence, and robustness to environmental variability. The experimental results show that optimized CNNs significantly outperform both unoptimized baselines and traditional machine learning models. The proposed optimization framework contributes to scalable and adaptive CNN deployment in precision agriculture, supporting more reliable and efficient crop health monitoring in UAV-integrated systems.

Bayesian optimization; Convolutional neural networks; Hyperparameter optimization; Particle swarm optimization; Precision agriculture; UAV-based crop monitoring

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