Predictive Neural Network Modeling for Almond Harvest Dust Control.

ABSTRACT: This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network's architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The r

SUBMITTER: Serajian R 

PROVIDER: S-EPMC11014124 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature