An Automatic Light Stress Grading Architecture Based on Feature Optimization and Convolutional Neural Network

Abstract

The identification of light stress is crucial for light control in plant factories. Image-based lighting classification of leafy vegetables has exhibited remarkable performance with high convenience and economy. Convolutional Neural Network (CNN) has been widely used for crop image analysis because of its architecture, high accuracy and efficiency. Among them, large intra-class differences and small inter-class differences are important factors affecting crop identification and a critical challenge for fine-grained classification tasks based on CNN. To address this problem, we took the Lettuce (Lactuca sativa L.) widely grown in plant factories as the research object and constructed a leaf image set containing four stress levels. Then a light stress grading model combined with classic pre-trained CNN and Triplet loss function is constructed, which is named Tr-CNN. The model uses the Triplet loss function to constrain the distance of images in the feature space, which can reduce the Euclidean distance of the samples from the same class and increase the heterogeneous Euclidean distance. Multiple sets of experimental results indicate that the model proposed in this paper (Tr-CNN) has obvious advantages in light stress grading dataset and generalized dataset.

Keywords

• light stress grading
• fine-grained visual classification
• inter-class variance
• intra-class variance
• feature mapping