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Front. Robot. AI | doi: 10.3389/frobt.2021.627009

Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels Provisionally accepted The final, formatted version of the article will be published soon. Notify me

  • 1IntelinAir, United States
  • 2Carnegie Mellon University, United States

Crop monitoring and yield prediction are central to management decisions for farmers. One key task is counting the number of kernels on an ear of corn to estimate yield in a field. As ears of corn can easily have 400-900 kernels, manual counting is unrealistic; traditionally, growers have approximated the number of kernels on an ear of corn through a mixture of counting and estimation. With the success of deep learning, these human estimates can now be replaced with more accurate machine learning models, many of which are efficient enough to run on a mobile device. Although a conceptually simple task, the counting and localization of hundreds of instances in an image is challenging for many image detection algorithms which struggle when objects are small in size and large in number. We compare different detection-based frameworks, Faster R-CNN, YOLO, and density-estimation approaches for on-ear corn kernel counting and localization. In addition to the YOLOv5 model which is accurate and edge-deployable, our density-estimation approach produces high-quality results, is lightweight enough for edge deployment, and maintains its computational efficiency independent of the number of kernels in the image. Additionally, we seek to standardize and broaden this line of work through the release of a challenging dataset with high-quality, multi-class segmentation masks. This dataset firstly enables quantitative comparison of approaches within the kernel counting application space and secondly promotes further research in transfer learning and domain adaptation, large count segmentation methods, and edge deployment methods.

Keywords: Counting, density estimation, precision agriculture, Dataset, YOLO, Machine vision application, edge deployment, UNET

Received: 07 Nov 2020; Accepted: 18 Mar 2021.

Copyright: © 2021 Hobbs, Khachatryan, Anandan, Hovhannisyan and Wilson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jennifer A. Hobbs, IntelinAir, Champaign, United States,