Image-based assessment of growth vigor for Phalaenopsis aphrodite seedlings using convolutional neural network

Abstract: In the Phalaenopsis industry, the growth vigor of seedlings when reaching their minimum growth time of vegetative cultivation plays an important role in the subsequent production chain and the final economic profits. The current manual assessment taking place in the commercial large-scale greenhouse is time-consuming and labor-intensive. Related studies based on RGB image for plant growth assessment relied on extracting hand-crafted features from images, affecting the effectiveness and generalization ability of machine learning models. In this study, the Convolutional Neural Network (CNN) was employed to explore its feasibility in assessing the growth vigor of Phalaenopsis aphrodite seedlings grown in the greenhouse in an end-to-end manner. Seedling images were collected in the greenhouse conditions with complex image background. Baseline models on the greenhouse dataset were established using different CNN architectures (VGG, ResNet, Inception-v3) coupled with various training mechanisms (training from scratch, fine-tuning, feature extraction), in which fine-tuning achieved the best classification results. Considering the target task of morphological classification for individual greenhouse seedlings with complex image background, to further boost model performance, additional seedlings images were acquired in controlled laboratory conditions. The segmented laboratory images were used to assist in model learning, namely building the augmented models. Two approaches were adopted, achieving an overall improvement in the testing F1-score of 0.03-0.05 compared with baseline models. The VGG model with augmentation method II achieved the highest performance in this study (F1-score of 0.997 on the test set), its feature maps were also visualized. In higher-level feature maps, regions of the target seedling were activated while filtering out most background including leaves from adjacent seedlings, proving the effective morphology characterizing using CNN for greenhouse seedlings. The overall results demonstrated the potential of deep learning models for image-based assessment of growth vigor for Phalaenopsis aphrodite seedlings and maybe other kinds of plants in greenhouse conditions.