Crop quantity assessment is crucial for making decisions concerning harvesting, yield estimation, and resource allocation. Traditional methods for estimating crop quantity involve time-consuming and labour-intensive manual measurements. Technology integration can aid in evaluating agricultural conditions and thus increase crop production. However, aerial imaging technology has enabled automating and speeding up this procedure. This chapter aims to explore aerial images’ use to assess the quantity of a crop field. It addresses the challenges associated with traditional methods of crop quantity assessment and introduces aerial imaging techniques such as unmanned aerial vehicles (UAVs) or drones and satellite imagery as promising tools for capturing high-resolution images of crop fields. Besides, the chapter discusses various image processing and analysis techniques employed to estimate crop quantity from aerial images. These methods comprise machine learning algorithms, feature extraction, and picture segmentation. In addition, the chapter explores integrating geographic information systems (GIS) with aerial imagery analysis to enhance crop quantity assessment. It discusses using spatial data, such as field boundaries, topography, and soil characteristics, to improve the accuracy of quantity estimation models. Factors such as weather conditions, image acquisition timing, and data processing requirements are discussed, along with potential solutions and future research directions.