A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications

Abstract

Improving soybean (Glycine max L. (Merr.)) yield is crucial for strengthening national food security. Predicting soybean yield is essential to maximize the potential of crop varieties. Non-destructive methods are needed to estimate yield before crop maturity. Various approaches, including the pod-count method, have been used to predict soybean yield, but they often face issues with the crop background color. To address this challenge, we explored the application of a depth camera to real-time filtering of RGB images, aiming to enhance the performance of the pod-counting classification model. Additionally, this study aimed to compare object detection models (YOLOV7 and YOLOv7-E6E) and select the most suitable deep learning (DL) model for counting soybean pods. After identifying the best architecture, we conducted a comparative analysis of the model’s performance by training the DL model with and without background removal from images. Results demonstrated that removing the background using a depth camera improved YOLOv7’s pod detection performance by 10.2% precision, 16.4% recall, 13.8% mAP@50, and 17.7% [email protected]:0.95 score compared to when the background was present. Using a depth camera and the YOLOv7 algorithm for pod detection and counting yielded a [email protected] of 93.4% and [email protected]:0.95 of 83.9%. These results indicated a significant improvement in the DL model’s performance when the background was segmented, and a reasonably larger dataset was used to train YOLOv7.

Keywords

• background segmentation
• computer vision
• deep learning
• depth camera
• high throughput phenotyping
• machine vision