###### Abstract  
Leaf coverage is an indicator of plant growth rate and predicted yield, and thus it is crucial to plant-breeding research. Robust image segmentation of leaf coverage from remote-sensing images acquired by unmanned aerial vehicles (UAVs) in varying environments can be directly used for large-scale coverage estimation, and is a key component of high-throughput field phenotyping. We thus propose an image-segmentation method based on machine learning to extract relatively accurate coverage information from the orthophoto generated after preprocessing. The image analysis pipeline, including dataset augmenting, removing background, classifier training and noise reduction, generates a set of binary masks to obtain leaf coverage from the image. We compare the proposed method with three conventional methods (Hue-Saturation-Value, edge-detection-based algorithm, random forest) and a frontier deep-learning method called DeepLabv3+. The proposed method improves indicators such as \(Q_{Seg}\), \(S_{r}\), \(E_{s}\) and mIOU by 15% to 30%. The experimental results show that this approach is less limited by radiation conditions, and that the protocol can easily be implemented for extensive sampling at low cost. As a result, with the proposed method, we recommend using red-green-blue (RGB)-based technology in addition to conventional equipment for acquiring the leaf coverage of agricultural crops. machine learning; maize-leaf coverage; image segmentation; UAV remoting images +
Footnote †: journal: Medical  
continuous imaging by fixed-position cameras, or destructive methods based on crop harvesting, are usually time-consuming. Moreover, since crop growth can vary between outdoor and indoor environments, indoor observations are not suitable for predicting outdoor growth trends. Numerous adverse factors affect the precision of field phenotypic observations, such as differences in nutrients and water availability. Moreover, environmental influences such as wind, humidity, changing solar radiation, and cloud coverage also degrade data accuracy. To accurately and reliably study the in-field growth pattern of plant cultivars, researchers use high-throughput field phenotyping (HTFP), whereby phenotypic parameters are acquired by using automated or semi-automated systems. Currently, most HTFP-based systems for estimating leaf coverage use multispectral or hyperspectral images or RGB images. In the present study, we focus on the use of RGB images, because the associated technology is much lighter and cheaper than a spectral system, and can be fixed to a small unmanned aerial vehicle (UAV) platform. Digital photography is a popular tool for acquiring field information about small crops because it is affordable and easy to use with minimal training. The key step of extracting leaf coverage from RGB images is image segmentation, and existing segmentation methods for RGB images focus mainly upon two aspects: The first aspect is solely based on color information. For example, [PERSON] et al. presented a technique that synergistically combines depth and color image information from real devices. They use the color information to fill and clean depth and use depth to enhance color-image segmentation [4]. [PERSON] and [PERSON] introduced a segmentation method that uses a color Gaussian-Markov random-field model, which considers both spatial interactions within each spectral band and the interactions between color planes [5]. [PERSON] et al. explored a bottom-up segmentation approach that was developed to segment randomly-textured color images [6], and [PERSON] et al. put color and texture information together in the segmentation process to finish the segmentation of synthetic and natural images [7]. In addition, [PERSON] et al. introduced a segmentation method that combines the hue-saturation-value (HSV) color space and the Otsu method. Their experimental results show that the algorithm presented herein has a good effect and can meet real-time demand [8].