Soybean pod and seed counting in both outdoor fields and indoor laboratories using unions of deep neural networks

TL;DR

This paper presents deep learning models for accurate soybean pod and seed counting in outdoor fields and indoor labs, improving robustness and accuracy through novel architectures and domain adaptation techniques.

Contribution

Developed and integrated advanced deep neural network architectures, including YOLO-SAM and YOLO-DA, for robust outdoor soybean seed counting, and used synthetic data with Mask-RCNN-Swin for precise indoor counting.

Findings

Outdoor MAE: 6.13 for pods, 10.05 for seeds

Indoor MAE: 1.07 for pods, 1.33 for seeds

Models outperform existing methods in accuracy and robustness

Abstract

Automatic counting soybean pods and seeds in outdoor fields allows for rapid yield estimation before harvesting, while indoor laboratory counting offers greater accuracy. Both methods can significantly accelerate the breeding process. However, it remains challenging for accurately counting pods and seeds in outdoor fields, and there are still no accurate enough tools for counting pods and seeds in laboratories. In this study, we developed efficient deep learning models for counting soybean pods and seeds in both outdoor fields and indoor laboratories. For outdoor fields, annotating not only visible seeds but also occluded seeds makes YOLO have the ability to estimate the number of soybean seeds that are occluded. Moreover, we enhanced YOLO architecture by integrating it with HQ-SAM (YOLO-SAM), and domain adaptation techniques (YOLO-DA), to improve model robustness and generalization across soybean images taken in outdoor fields. Testing on soybean images from the outdoor field, we achieved a mean absolute error (MAE) of 6.13 for pod counting and 10.05 for seed counting. For the indoor setting, we utilized Mask-RCNN supplemented with a Swin Transformer module (Mask-RCNN-Swin), models were trained exclusively on synthetic training images generated from a small set of labeled data. This approach resulted in near-perfect accuracy, with an MAE of 1.07 for pod counting and 1.33 for seed counting across actual laboratory images from two distinct studies.