Artificial Neural Networks (ANNs) are increasingly recognized as valuable tools for crop quality parameter measurement. This study investigates the ANNs effectiveness in the predictive measurement of the Chlorophyll-a levels of iceberg lettuce (Lactuca sativa var. capitata). This involved using ANNs to link the dataset of extracted RGB and HSV values with the Chlorophyll-a levels retrieved with UV - VIS spectroscopy. For the prediction model, the RGB and HSV values were used as the 6 input predictor values, while the Chlorophyll-a level was used as the 1 output response value. The ANN s were trained on this dataset using the Levenberg-Marquardt algorithm, where the training data comprised 70% of the dataset, the validation data 20% of the dataset, and the test data 10% of the dataset with a layer size of 15. The ANN model demonstrated a strong correlation between the predicted and target outputs, with an accuracy of 98.02% for the test data. This suggests that ANNs can be employed for an accurate and non-invasive monitoring of parameters in iceberg lettuce. The findings also open possibilities for other crops in the Philippines' agricultural industry.