**1. Introduction**

Maize (*Zea mays* L.) is a monoecious crop, and the tassel is a branched structure atop the plan. The tassels' size and shape (branch number, compactness, etc.) have influence on the yield and

quality of maize through affect the amount of pollen produced, the plant nutrition supply and the light interception of lower leaves [1,2], and such influence will be more significant with the increase of planting density [3,4]. In addition, in order to ensure the purity and quality of maize hybrid seed, cross-pollination should be ensured in the tasseling stage of seed maize production field, so tassels of female plants have to be removed through artificial or mechanical process to prevent self-pollination [5–7]. Therefore, tassel development has always been one of the important phenotypic traits in maize breeding and seed production. Rapid and accurate detection of tassel development status and branch number during maize flowering stage is of great significant for maize production management, and it is also of practical value for the arrangement of detasseling.

The complex planting environment in the field, such as uneven illumination conditions, leaves covered tassels severely and obvious difference between varieties, has brought great challenges to the automatic detection of maize tassels. Traditional crop phenotype acquisition is dependent on artificial field measurement, which is labor-intensive and time-consuming [8,9], and moreover, there is no uniform standard for phenotypical data collection, so the data is subjective. With the improvement of computer performance and the development of image processing, it is possible to extract crop phenotypic traits quickly and automatically. According to the different data acquisition platforms, the tassels detection research in field environment is mainly based on fixed observation tower and unmanned aerial vehicles (UAVs) high-throughput phenotype platform [10–14].

Compared with platform based on ground vehicles [15], the acquisition of images by cameras installed on fixed observation tower or cable-suspend platform is not limited by crop types and soil conditions (for example, the irrigated soil will hinder the mobile vehicle to collect data), which is suitable for real-time monitoring in the field [16,17]. Lu Hao et al. obtained sequence images of maize tassels by using a camera installed on an observation tower, and each sequence covered the tasseling stage to the flowering stage. On this basis, they carried out studies on tassel identification [11], and maize tassels counting which was based on the local counts regression [18]. In terms of distinguishing features, Hue component image in Hue-Saturation-Intensity (HSI) color space has been proved to be able to distinguish tassels from leaves [12,19]. Mao Zhengchong et al. [12] successfully separated the approximate region of tassels through binarization processing and median filtering of Hue component image, and then used LVQ neural network to eliminate the false detection regions, with the final detection accuracy reaching 96.9%. This kind of method can achieve high accuracy, but it is difficult to apply to large scale breeding field because its data acquisition and processing methods are low throughput, and equipment is expensive.

In recent years, UAVs equipped with different sensors have attracted extensive attention from researchers and breeders due to their advantages such as flexibility, high spatial and temporal resolution, and relatively low cost [17,20]. UAVs high-throughput phenotype platform can realize phenotypic analysis on many breeding plots in real time and dynamically, and is regarded as an indispensable tool for plant breeding [21–23]. Some scholars have studied sorghum heads detection [24,25], digital counts of crop plants at early stage [26–28], tree canopy identification through UAV images [29,30], and crop disease monitoring [31,32]. There are two studies on maize tassels detection based on UAV platform, Zhang Qi [13] divided UAV image into several small objects by object oriented classification method, and then identified tassels with vegetation index and spectrum information, with the overall accuracy reaching 85.91%. Yunling Liu et al. [14] used faster region-based convolutional neural network (Faster R-CNN) with ResNet and VGGNet to detect maize tassels, founded that the ResNet as the feature extraction network, was better than the VGGNet, and the detection accuracy can reach 89.96%. Compared with fixed observation tower, UAVs high-throughput phenotype platform is flexible, low-cost and has the potential to be applied in large scale. However, the above methods did not identify tassels in different tasseling stages, which causes the detection scene is single and difficult to meet the demand of dynamic monitoring for tassel development. Moreover, these methods terminate in the detection stage, without more detailed analysis (such as the

morphological characteristics of tassels etc.) that made it impossible to show the tassels' development to breeders or production managers intuitively.

To know tassels' development status in maize breeding fields and seed maize production fields in time, and also provide decision support for varieties selection and the detasseling arrangement, a novel tassels detection method suitable for complex scenes is urgently needed. Therefore, the objective of this study is to propose an accurate method for tassels detection that suitable for different maize varieties and tasseling stages based on time series UAV images (from tasseling begins to ends of all breeding plots). Considering that maize tassels vary greatly in shape and size as plant grow over time, and unsynchronized growth stage between the plots, etc. in this study, we first divided images into tassel regions and non-tassel regions by using random forest (RF), and then extracted the potential tassel region proposals by morphological method; afterwards, false positives were removed through VGG16 network, and the influence of different tasseling stages on the model accuracy was analyzed; finally, we proposed an endpoint detection method to explore how to apply detection results to the extraction of tassel branch number.

#### **2. Materials and Methods**

#### *2.1. Data Acquisition*

The field trial was in Juqiao Town, Hebi City, Henan Province, China. The trial area was approximately 34.5 m from north to south and 72 m from east to west, including 170 breeding plots with a size of 2.4 m × 5 m. The plant density of these plots was 67500 plants/ha, and the row spacing was 0.6 m. In this trial, 167 maize varieties of different genetic backgrounds were sown on 17 June 2019, among which Zhengdan958 was repeated 4 times as the check variety, while the others were not repeated (Figure 1).

**Figure 1.** The layout of experimental field.

To improve the image accuracy of UAV in this study, 15 square panels with the size of 50 cm × 50 cm were deployed in the field (Figure 1) to be used as ground control points (GCPs) and measured by Real-time kinematic (RTK) technology (i80, Shanghai Huace Navigation Technology Ltd., Shanghai, China) with centimeter-level accuracy. A DJI ZENMUSE X4s camera (resolution: 5472 × 3648, DJI-Innovations, Inc., Shenzhen, China) was installed on the DJI Inspire 2 drone (DJI-Innovations, Inc.) to record RGB images. The UAV's flight path was set by DJI GSPro software in advance, with 85% forward overlap and 85% lateral overlap. To ensure that the model proposed in this study can realize the tassels detection of different varieties and different tasseling stages, we collected 5 groups of data for 4 days from 7 August 2019 to 12 August 2019 (no image data was collected on 10 and 11 August, due to the strong wind and light rain respectively, and 2 groups of image data were collected at 9:00 and 14:00 on 8 August). The flight altitude was set to 20 m, and about 500 digital images were collected for each flight, so a total of about 2500 images were collected.
