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

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.

Plant phenotyping relevance

トウモロコシ幼苗の個体数という植物形質を、画像動画とYOLOv3・Kalman filterで自動取得・計数する手法の開発が中心であり、精度と処理速度も評価している。

Code and data availability

The paper describes corn stand counting videos, labeled images, and YoloV3/Kalman pipeline, but the Data Availability Statement says 'Data sharing not applicable.' No public dataset, code, model, or supplement URL is provided. The only external URL (sci.sdsu.edu) is a cited reference on plant sampling methods, not a论文-

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