The official repository for the paper: Scalable learning for bridging the species gap in image-based plant phenotyping.
The code for the Doing More With Less: A Multi-Task Deep Learning Approach in Plant Phenotyping paper, Frontiers in Plant Science
My friends Nikhil Vijay S, Harish Kumar Patidar and I did work on the leaf segmentation challenge, Mentored by Mr.Mohit Agarwal on an internship at Bennett University. In this project, we investigate the problem of segmenting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of- the-art deep learning architectures: UNET, a convolutional neural network for initial segmentation. Evaluation is performed on the leaf segmentation challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets, we obtain satisfactory performance on segmenting leaves from the background as a whole and counting the number of leaves using simple data augmentation strategies. Comparative analysis is provided against methods evaluated on the previous competition datasets.
