# Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications

arXiv: https://arxiv.org/abs/2011.07118 · HTML: https://mlsanity.com/paper/2011.07118/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images](https://mlsanity.com/paper/2310.10861/) (2023)
- [Agri-GNN: A Novel Genotypic-Topological Graph Neural Network Framework Built on GraphSAGE for Optimized Yield Prediction](https://mlsanity.com/paper/2310.13037/) (2023)
- [High Throughput Soybean Pod-Counting with In-Field Robotic Data Collection and Machine-Vision Based Data Analysis](https://mlsanity.com/paper/2105.10568/) (2021)
- [CongNaMul: A Dataset for Advanced Image Processing of Soybean Sprouts](https://mlsanity.com/paper/2308.15690/) (2023)
- [ViewSparsifier: Killing Redundancy in Multi-View Plant Phenotyping](https://mlsanity.com/paper/2509.08550/) (2025)
- [SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes](https://mlsanity.com/paper/2606.19623/) (2026)
- [Robust soybean seed yield estimation using high-throughput ground robot videos](https://mlsanity.com/paper/2412.02642/) (2024)
- [Soybean pod and seed counting in both outdoor fields and indoor laboratories using unions of deep neural networks](https://mlsanity.com/paper/2502.15286/) (2025)
- [Can Synthetic Data Overcome the Generalization Limits of AI-Based Flower and Pod Detection Across Cowpea Breeding Genotypes and Environments?](https://mlsanity.com/paper/2607.28796/) (2026)

## Abstract (arXiv)

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean [textitGlycine max L. (Merr.)] pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multi-view image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort, and opening new breeding methods avenues to develop cultivars.