ABSTRACT
Seasonal vegetables play a crucial role in both nutrition and commerce in Bangladesh. Recognizing this significance, our research introduces the 'SeasVeg' dataset, comprising images of ten varieties of seasonal vegetables sourced from Dhaka and Pabna regions. These include Carica papaya, Momordica dioica, Abelmoschus esculentus, Lablab purpureus, Trichosanthes cucumerina, Trichosanthes dioica, Solanum lycopersicum, Brassica oleracea, Momordica charantia, and Raphanus sativus. Our dataset encompasses 4500 images, 1500 original and 3000 augmented, meticulously captured under natural light conditions to ensure authenticity. While our primary focus lies in leveraging machine learning and deep learning techniques for advancements in agriculture science, particularly in aiding healthcare aspects with seasonal vegetables and nutrition's, we acknowledge the versatile utility of our dataset. Beyond healthcare, it serves as a valuable educational resource, facilitating children's and toddlers' learning to identify these vital vegetables. This dual functionality broadens the dataset's appeal and underscores its societal impact beyond the realm of healthcare. Besides, the research culminates in the implementation of machine learning models, achieving noteworthy accuracy. We get the highest 99 % accuracy with the ResNet50 pre-trained CNN model and a good 94 % accuracy with the InceptionV3 pre-trained CNN model when it comes to the computer-aided vegetable classification. However, the 'SeasVeg' dataset represents not only a significant stride in healthcare innovation but also a promising tool for educational endeavors, catering to diverse stakeholders and fostering interdisciplinary collaboration.