A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

dc.contributor.author Wang, Le
dc.contributor.author Xiang, Lirong
dc.contributor.author Tang, Lie
dc.contributor.author Jiang, Huanyu
dc.contributor.department Agricultural and Biosystems Engineering
dc.date.accessioned 2023-01-18T18:38:51Z
dc.date.available 2023-01-18T18:38:51Z
dc.date.issued 2021-01-13
dc.description.abstract Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting.
dc.description.comments This article is published as Wang, Le, Lirong Xiang, Lie Tang, and Huanyu Jiang. "A convolutional neural network-based method for corn stand counting in the field." Sensors 21, no. 2 (2021): 507. DOI: 10.3390/s21020507. Copyright 2021 by the authors. Attribution 4.0 International (CC BY 4.0). Posted with permission.
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/Nr1VXy1z
dc.language.iso en
dc.publisher MDPI
dc.source.uri https://doi.org/10.3390/s21020507 *
dc.subject.disciplines DegreeDisciplines::Engineering::Bioresource and Agricultural Engineering
dc.subject.disciplines DegreeDisciplines::Physical Sciences and Mathematics::Computer Sciences::Artificial Intelligence and Robotics
dc.subject.keywords deep learning
dc.subject.keywords YoloV3
dc.subject.keywords video tracking
dc.subject.keywords corn stand counting
dc.title A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field
dc.type Article
dspace.entity.type Publication
relation.isAuthorOfPublication e60e10a5-8712-462a-be4b-f486a3461aea
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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