Vision-Based Chlorophyll-a Measurement for Iceberg Lettuce Using Levenberg-Marquardt-Optimized Shallow Neural Network

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.