###### Abstract  
Monitoring the cotton budding rate is important for growers so that they can replant cotton in a timely fashion at locations at which cotton density is sparse. In this study, a true-color camera was mounted on an unmanned aerial vehicle (UAV) and used to collect images of young cotton plants to estimate the germination of cotton plants. The collected images were preprocessed by stitching them together to obtain the single orthomosaic image. The support-vector machine method and maximum likelihood classification method were conducted to identify the cotton plants in the image. The accuracy evaluation indicated the overall accuracy of the classification for SVM is 96.65% with the Kappa coefficient of 93.99%, while for maximum likelihood classification, the accuracy is 87.85% with a Kappa coefficient of 80.67%. A method based on the morphological characteristics of cotton plants was proposed to identify and count the overlapping cotton plants in this study. The analysis showed that the method can improve the detection accuracy by 6.3% when compared to without it. The validation based on visual interpretation indicated that the method presented an accuracy of 91.13%. The study showed that the minimal resolution of no less than 1.2 cm/pixel in practice for image collection is necessary in order to recognize cotton plants accurately. SVM; budding rate; UAV +
Footnote †: journal: Applied  
and limited image resolution is not adequate for some studies, e.g., crop phenotyping and crop germination monitoring, which require resolutions greater than 1 cm [5; 6]. In addition, for applications that require regular observation of a specific area, e.g., daily, the long repeat visit cycle is another problem when relying on high resolution satellite data [7; 8]. Recent technological improvements make use of multirotor and fixed-wing unmanned aerial vehicles (UAVs) as a convenient information acquisition method for scientific researchers and commercial operators [9; 10]. Compared to the ground data collection method, e.g., robots or tractors [11; 12], the main difference is that the UAV can work well without touching the ground. This is important for areas in which the ground is covered by plastic in order to decrease evapotranspiration and increase the ground temperature. Additionally, compared to satellite data, the most important advantages of UAVs are that the flight altitude can be controlled by the user, and the sensor devices, e.g., LiDAR devices, hyperspectral cameras, and true-color cameras, loaded on the UAV can be changed according to the user's purposes. As a result, the resolution of the observation data (resolution greater than 1 cm) satisfies the requirements of many applications. Due to the versatility and flexibility of UAVs, numerous studies have adopted the UAVs as a basic information collection tool. For example, [PERSON] et al. used UAV images and the maximum likelihood classification method to examine the germination rate of cotton seeds at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, TX [13]. [PERSON] et al. used thermal and narrowband multispectral cameras on a UAV to monitor vegetation [14]. [PERSON] and [PERSON] used UAV orthomosaics at a resolution of 5 cm to map wild leeks, an endangered plant species of Eastern North America [15]. [PERSON] et al. measured vegetation height with UAV photogrammetry to monitor vegetation recovery [5]. [PERSON] et al. used a UAV and a modified normalized difference vegetation index (NDVImod) for Chlorophyll Detection in Water Bodies [16]. Among these researches, in order to achieve the recognition of the plants or interested objects, several imagery classification methods have been adopted, e.g., the support vector machine (SVM), random decision forests (RF), neural network, Bayesian network, and the maximum likelihood classification method [17; 18; 19; 20; 21; 22; 23].