Climate change is increasingly disrupting worldwide agriculture, making
global food production less reliable.To tackle the growing challenges in
feeding the planet, cutting-edge management strategies, such as precision
agriculture, empower farmers and decision-makers with rich and actionable
information to increase the efficiency and sustainability of their farming
practices.Crop-type maps are key information for decision-support tools but are
challenging and costly to generate.We investigate the capabilities of Meta AI's
Segment Anything Model (SAM) for crop-map prediction task, acknowledging its
recent successes at zero-shot image segmentation.However, SAM being limited to
up-to 3 channel inputs and its zero-shot usage being class-agnostic in nature
pose unique challenges in using it directly for crop-type mapping.We propose
using clustering consensus metrics to assess SAM's zero-shot performance in
segmenting satellite imagery and producing crop-type maps.Although direct
crop-type mapping is challenging using SAM in zero-shot setting, experiments
reveal SAM's potential for swiftly and accurately outlining fields in satellite
images, serving as a foundation for subsequent crop classification.This paper
attempts to highlight a use-case of state-of-the-art image segmentation models
like SAM for crop-type mapping and related specific needs of the agriculture
industry, offering a potential avenue for automatic, efficient, and
cost-effective data products for precision agriculture practices.

全球气候变化对农业产生越来越大的影响，因此为了解决粮食生产中日益增长的挑战，最前沿的管理策略，如精确农业，为农民和决策者提供丰富而实用的信息，以提高农业实践的效率和可持续性。本文研究了 Meta AI 公司的 Segment Anything Model (SAM) 在作物地图预测任务中的能力，并提出使用聚类一致性指标评估 SAM 在分割卫星图像和生成作物类型地图中的零样本性能。尽管在零样本设置下直接进行作物类型映射具有挑战性，但实验显示 SAM 在迅速准确地勾勒卫星图像中的农田方面具有潜力，为后续作物分类奠定了基础。本文试图突出说明像 SAM 这样的最新图像分割模型在作物类型映射和相关农业行业的特定需求方面的应用案例，为精密农业实践提供了实现自动化、高效和具有成本效益的数据产品的潜在途径。

能否识别作物？通过卫星图像生成精准农业的作物类型地图时，量化语义分割基础模型的零样本性能

Can SAM recognize crops? Quantifying the zero-shot performance of a  semantic segmentation foundation model on generating crop-type maps using  satellite imagery for precision agriculture