Abstract
The Caribbean region is home to, and widely known for, its many “hot" peppers. These peppers are now heavily researched to bolster the development of the regional pepper industry. However, accurately identifying the different landraces of peppers in the Caribbean has since remained an arduous, manual task that involves the physical inspection and classification of individual peppers. An automated approach that uses machine-learning techniques can help with this task; however, machine learning approaches require vast amounts of data to work well. This paper presents a new multi-label annotated, image-dataset of Capsicum Chinense peppers from Trinidad and Tobago. The paper also presents a benchmark for image-pepper classification and identification. It serves as a starting ground for future work that can include the compilation of larger datasets of regional peppers that can include more morphological features. It additionally serves as the starting ground for a Caribbean-based hot-pepper ontology.
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Mungal, J., Daniel, A., Mohammed, A., Mohammed, P. (2023). An Annotated Caribbean Hot Pepper Image Dataset. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_49
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