Crop area estimation plays a crucial role in agriculture, where farming is a significant contributor to the economy of several countries. Accurate estimation of crop areas helps farmers, policymakers, and researchers make informed decisions regarding crop management, resource allocation, and market forecasting. With the use of drones, this process has become more efficient and precise. Drones equipped with a camera can capture high-resolution images of agricultural fields, allowing for detailed analysis and mapping of crop areas. UAV-captured images offer superior spatial resolutions, marking a departure from previous single-crop classifications and opening doors to the realm of multi-crop classification. The state-of-the-art methods of automated crop monitoring are based on satellite images, which are difficult to get by the farmers. We propose a crop monitoring system using drone images, which are easily obtainable by the farmers. The primary objective of this paper is to estimate the area of the crops accurately, from the drone images. We identify the crop area by applying a region-growing algorithm. Later we use a CNN classification model to classify the crops as “horse gram”, “tomato”, and “fallow land”. We obtain an automated system to identify the crop regions and monitor the health of individual plants and crop areas using aerial drone images. We experiment with several existing models ranging from the basic Convolutional Neural Network (CNN) to pre-trained models such as VGG, ResNet, and InceptionNet and vision transformers (ViT). The objective of the paper is to generate a dataset of annotated images to be used for research on computer vision-based techniques for crop monitoring. Further, we find a suitable deep learning model to apply to the proposed dataset for efficient crop monitoring. The proposed annotated dataset is available at https://drive.google.com/drive/folders/1qCAFrPwb7R8tOdh_9BYxe2OmOtDJNOKp?usp=sharing . The codes related to this study are available at https://colab.research.google.com/drive/1A5vCq6tgQ1IPe3LIAiT7tuQsoq4AAMN3?usp=sharing