Seed Kernel Counting using Domain Randomization and Object Tracking Neural Networks

TL;DR

This paper presents a cost-effective seed kernel counting method using synthetic training data, neural networks, and object tracking algorithms to accurately estimate cereal yield from videos.

Contribution

It introduces a novel approach combining synthetic imagery with object tracking neural networks for seed counting, reducing the need for extensive labeled data.

Findings

Achieved over 95% accuracy in seed counting for Soy and Wheat.

Demonstrated effectiveness of synthetic data for training neural networks.

Compared StrongSORT and ByteTrack algorithms, with ByteTrack slightly outperforming StrongSORT.

Abstract

High-throughput phenotyping (HTP) of seeds, also known as seed phenotyping, is the comprehensive assessment of complex seed traits such as growth, development, tolerance, resistance, ecology, yield, and the measurement of parameters that form more complex traits. One of the key aspects of seed phenotyping is cereal yield estimation that the seed production industry relies upon to conduct their business. While mechanized seed kernel counters are available in the market currently, they are often priced high and sometimes outside the range of small scale seed production firms' affordability. The development of object tracking neural network models such as You Only Look Once (YOLO) enables computer scientists to design algorithms that can estimate cereal yield inexpensively. The key bottleneck with neural network models is that they require a plethora of labelled training data before they can be put to task. We demonstrate that the use of synthetic imagery serves as a feasible substitute to train neural networks for object tracking that includes the tasks of object classification and detection. Furthermore, we propose a seed kernel counter that uses a low-cost mechanical hopper, trained YOLOv8 neural network model, and object tracking algorithms on StrongSORT and ByteTrack to estimate cereal yield from videos. The experiment yields a seed kernel count with an accuracy of 95.2\% and 93.2\% for Soy and Wheat respectively using the StrongSORT algorithm, and an accuray of 96.8\% and 92.4\% for Soy and Wheat respectively using the ByteTrack algorithm.