Detection of abnormal hydroponic lettuce leaves based on image processing and machine learning

Abstract

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting. Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce. This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models, i.e. Multiple Linear Regression (MLR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). One-way analysis of variance was applied to reduce RGB, HSV, and L*a*b* features number of hydroponic lettuce images. Image binarization, image mask, and image filling methods were employed to segment hydroponic lettuce from an image for models testing. Results showed that G, H, and a* were selected from RGB, HSV, and L*a*b* for training models. It took about 20.25 s to detect an image with 3 024 × 4 032 pixels by KNN, which was much longer than MLR (0.61 s) and SVM (1.98 s). MLR got detection accuracies of 89.48% and 99.29% for yellow and rotten leaves, respectively, while SVM reached 98.33% and 97.91%, respectively. SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic. Thus, it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.

Keywords

• Hydroponic lettuce
• Machine learning
• Image processing
• Abnormal leaves
• Automatic sorting