SeedAI: a novel seed germination predictionsystem using dual stage deep learning framework

Abstract

The detection of germinating seeds through automated means is a significant concern for seed testing agencies. Traditional approaches employ inspection manually. In recent years, there has been an increasing scientific focus on deep learning, particularly in the domain of seed detection, recognition, and germination in germination trays. In this paper, a novel two-stage network is proposed which leverages various Convolutional Neural Networks (CNN) to automate detection of seeds and the assessment of their germination state. In the first stage Mask R-CNN framework is used for instantaneous segmentation of seeds and in the next stage this Region of Interest (RoI) is given as input to the proposed CNN model for germination prediction. The proposed model is trained and tested on our own dataset. The experimental results proved that our proposed model is achieved better performance than state-of-the-art models with less trainable parameters.

Keywords

• seed quality
• germination
• mask R-CNN
• detectron 2
• deep learning