In South Asian countries, agriculture is a crucial employment field, and a remarkable number of people depend on it for their livelihood. Crop diseases are a significant threat to sustainable development in the agriculture field. Automated efficient crop disease diagnosis techniques developed with comprehensive field image datasets can play a vital role in preventing diseases at an early stage. Betel leaf is widely consumed in South Asian countries for its nutritional benefits, but to the best of our knowledge, no extensive dataset of betel leaf is available that can play a crucial role in developing accurate disease diagnosis tools. Farmers face a significant economic loss due to betel leaf diseases, and due to the lack of efficient diagnosis tools, the farming of betel leaf has become very difficult day by day. Our motive is to develop a reliable and versatile image dataset of field images that will assist artificial intelligence-based pathology research on betel leaf diseases. This dataset contains healthy leaf images and two common disease images of betel leaf such as leaf rot and leaf spot [1]. Initially, 2,037 betel leaf images were captured in a natural daylight environment from several betel cultivation fields in Bangladesh. Afterward, 10,185 images were generated using image augmentation strategies including flipping, brightness factor, contrast factor, and rotation. This dataset is well-compatible with machine learning and deep learning-based pathology research, as it contains enough image samples for model training, validation, and testing. Moreover, a comparison study is conducted that ensures this dataset fulfills the gap of a reliable and extensive dataset of betel leaf. This comprehensive dataset serves as a crucial resource for researchers in developing efficient computational models for accurate betel leaf disease diagnosis.