A general Seeds-Counting pipeline using deep-learning model

Abstract

This study presents a novel Seeds-Counting pipeline harnessing deep learning algorithms to facilitate the automation of yield prediction prior to harvesting, a crucial component of the breeding process. Unlike existing methods that often cater to a single seed species or those with similar shapes, our approach is capable of accurately estimating the number of seeds across a diverse range of species. The pipeline incorporates a classification network for seed image categorization, along with object detection models specifically tailored to accommodate the morphologies of different seeds. By integrating a seed classifier, three distinct seed detectors, and post-processing filters, our method not only showcases exceptional accuracy but also exhibits robust generalization capabilities across various conditions. Demonstrating an error rate of less than 2% in the test set and achieving accuracy rates exceeding 97% in the extended set, the proposed pipeline offers a viable and efficient solution for high-throughput phenotyping and precision agriculture, effectively overcoming the challenges posed by the diverse morphologies of seeds.