# DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation

arXiv: https://arxiv.org/abs/2007.10521 · HTML: https://mlsanity.com/paper/2007.10521/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [Semi-Supervised Learning with Pseudo-Negative Labels for Image Classification](https://mlsanity.com/paper/2301.03976/) (2023)
- [3D Reconstruction-Based Seed Counting of Sorghum Panicles for Agricultural Inspection](https://mlsanity.com/paper/2211.07748/) (2022)
- [Detecting Olives with Synthetic or Real Data? Olive the Above](https://mlsanity.com/paper/2308.08271/) (2023)
- [Scalable Vision-Guided Crop Yield Estimation](https://mlsanity.com/paper/2511.12999/) (2025)
- [An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions](https://mlsanity.com/paper/2108.00952/) (2021)
- [Automated Seed Quality Testing System using GAN & Active Learning](https://mlsanity.com/paper/2110.00777/) (2021)
- [Deep Subspace analysing for Semi-Supervised multi-label classification of Diabetic Foot Ulcer](https://mlsanity.com/paper/2110.01795/) (2021)
- [Hinting Pipeline and Multivariate Regression CNN for Maize Kernel Counting on the Ear](https://mlsanity.com/paper/2306.06553/) (2023)

## Abstract (arXiv)

The success of modern farming and plant breeding relies on accurate and efficient collection of data. For a commercial organization that manages large amounts of crops, collecting accurate and consistent data is a bottleneck. Due to limited time and labor, accurately phenotyping crops to record color, head count, height, weight, etc. is severely limited. However, this information, combined with other genetic and environmental factors, is vital for developing new superior crop species that help feed the world's growing population. Recent advances in machine learning, in particular deep learning, have shown promise in mitigating this bottleneck. In this paper, we propose a novel deep learning method for counting on-ear corn kernels in-field to aid in the gathering of real-time data and, ultimately, to improve decision making to maximize yield. We name this approach DeepCorn, and show that this framework is robust under various conditions. DeepCorn estimates the density of corn kernels in an image of corn ears and predicts the number of kernels based on the estimated density map. DeepCorn uses a truncated VGG-16 as a backbone for feature extraction and merges feature maps from multiple scales of the network to make it robust against image scale variations. We also adopt a semi-supervised learning approach to further improve the performance of our proposed method. Our proposed method achieves the MAE and RMSE of 41.36 and 60.27 in the corn kernel counting task, respectively. Our experimental results demonstrate the superiority and effectiveness of our proposed method compared to other state-of-the-art methods.