# Evaluating the Efficacy of Segment Anything Model for
## 1 Introduction  
The estimation of assets and the prediction of yields for agricultural crops have become integral components of achieving precision agriculture management. For example, [PERSON] et al. [1] investigated the nonlinear relationship between wheat yield andenvironmental variables in India. They leveraged multi-source remote sensing data and deep learning methods in an attempt to utilize remote sensing for accurate crop yield prediction. In a separate study, [PERSON] et al. [2] employed a U-Net segmentation network model and random forest to analyze crop yield estimation in the study area under diverse climatic conditions. Notably, remote sensing data played a crucial role as a significant support for their analysis. In order to quickly and efficiently obtain relatively accurate agricultural remote sensing data, it is essential to accurately distinguish each type of crop distribution. Similarly, with increasing focus on urban green spaces (UGSs), such as botanical gardens, higher education campuses, and community parks and gardens, in the wake of urban heat island effects [3] and regional warming, it becomes relevant to investigate image segmentation techniques for quick and reliable delineation of UGSs in aerial and satellite images. The derived UGS outlines act as direct input to various urban simulation models. For instance, [PERSON] et al. [4] employed a cellular automata model to simulate and predict the urban green space system in the study area over a span of more than 10 years. The data utilized included land use maps detailing green spaces and plazas. [PERSON] et al. [5] utilized Landsat remote sensing satellite data to capture images depicting the distribution of urban green spaces and other relevant areas. By integrating processed land classification results from land maps with a future land use simulation model, they predicted the distribution and morphology of future urban green spaces (UGSs) in the study area. This approach aimed to investigate the prospective development of urban heat island intensity in the city. The predictions regarding distribution and morphology were particularly valuable in exploring the future evolution of urban heat island intensity in the city. Consequently, the utilization of very high spatial resolution images, captured using unmanned aerial vehicles (UAVs), for semantic segmentation of remote sensing images has emerged as a crucial area of research [6]. The high spatial resolutions of UAV images [7; 8; 9; 10] enable efficient differentiation of various crop and vegetation types and facilitate partition analysis. Serving as the cornerstone of UGS and Agricultural Remote Sensing Image (ARSI) analysis, continual enhancements in image segmentation algorithms are being pursued in an effort to enhance mapping precision and accuracy. Currently, numerous research directions have been explored in the field of semantic segmentation techniques for ARSI. However, a unique, precise algorithmic model or method that can expedite mapping and monitoring has yet to emerge. The main research focus until now has been on adopting diverse methods to address case-specific scenarios. For instance, in tackling the bisection problem, [PERSON] et al. [11] successfully differentiated between weeds and crops through the application of a discrete wavelet transform-based algorithm. This algorithm demonstrates enhanced capabilities in spatial texture and color feature extraction, providing a notable advantage over the conventional threshold segmentation technique that relies solely on color. Meanwhile, [PERSON] and [PERSON] [12] employed an object-based approach for vegetation segmentation and complemented it with traditional machine learning classifiers to achieve diverse crop classification, yielding favorable results. Beyond the conventional threshold-based and region-based image segmentation methods, there is a growing adoption of deep learning-based algorithms in the realm of image segmentation.