Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images.

Abstract

Ear density, or the number of ears per square meter (ears/m 2 ), is a central focus in many cereal crop breeding programs, such as wheat and barley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized technique for assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could even be used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear density assessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant, or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographically across Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. The three-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps: (i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation and counting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the camera resolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%) and R 2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.

Plant phenotyping relevance

圃場RGB画像から穂密度を自動推定する画像取得・画像処理・計数アルゴリズムを開発し、手動計数と比較検証した研究であり、植物フェノタイピング手法が中心です。

Code and data availability

The paper's ear-counting analysis code is distributed as the authors' CerealScanner Fiji plugin, with a code repository URL listed, but the text explicitly states access permissions must be obtained from the corresponding authors, so it is not directly public. No public phenotype dataset or image collection is offered.

No evidence-backed public reproduction asset is currently recorded.