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.

Automatic wheat ear counting using machine learning based on RGB UAV imagery

“…To date, automatic ear‐counting systems, regardless of the acquisition equipment, have been evaluated from the ground, using only a portion of the area of the plot (Cointault et al ., 2008; Zhu et al ., 2016; Sadeghi‐Tehran et al ., 2017; Velumani et al ., 2017; Zhou et al ., 2018a,b; Fernandez‐Gallego et al ., 2018a,b; Madec et al ., 2019; Fernandez‐Gallego et al ., 2019a, b). Although the use of a UAV platform allows for the acquisition of the complete area of the phenotyping micro‐plots, multispectral, thermal and laser sensors, with fairly low spatial resolution from aerial platforms, all remain relatively costly.…”

Wheat ear counting using K-means clustering segmentation and convolutional neural network

“…Cointault et al [10] used a color texture image analysis method based on mixed space to realize the recognition and counting of wheat ear. Fernandez-Gallego et al [11] used local maximum peak values to count ears based on RGB color images in field conditions [12]. The current recognition methods based on image processing technology require extensive artificial image feature extraction, which places high demand on the environment and technology.…”