Seed Kernel Counting using Deep Learning for High-Throughput Phenotyping

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 [1]. One of the key aspects of seed phenotyping is cereal yield estimation that the seed packaging industry relies upon to conduct their business. While mechanized seed kernel counters are available in the market, they are often priced high and outside the affordability realm of small-scale seed packaging firms. 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 of neural network models is that they require a plethora of labeled 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 of StrongSORT and ByteTrack to estimate cereal yield from videos captured using a mobile phone. 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 accuracy of 96.8% and 92.4% for Soy and Wheat respectively using the ByteTrack algorithm.