Tea leaf age quality: Age-stratified tea leaf quality classification dataset

Md Mohsin Kabir; Md Sadman Hafiz; Shattik Bandyopadhyaa; Jamin Rahman Jim; M F Mridha

doi:10.1016/j.dib.2024.110462

Tea leaf age quality: Age-stratified tea leaf quality classification dataset

Data Brief. 2024 Apr 21:54:110462. doi: 10.1016/j.dib.2024.110462. eCollection 2024 Jun.

Authors

Md Mohsin Kabir¹, Md Sadman Hafiz², Shattik Bandyopadhyaa², Jamin Rahman Jim³, M F Mridha³

Affiliations

¹ Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.
² Institute of Information and Communication Technology, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh.
³ Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh.

Abstract

The "Tea Leaf Age Quality" dataset represents a pioneering agricultural and machine-learning resource to enhance tea leaf classification, detection, and quality prediction based on leaf age. This comprehensive collection includes 2208 raw images from the historic Malnicherra Tea Garden in Sylhet and two other gardens from Sreemangal and Moulvibajar in Bangladesh. The dataset is systematically categorized into four distinct classes (T1: 1-2 days, T2: 3-4 days, T3: 5-7 days, and T4: 7+ days) according to age-based quality criteria. This dataset helps to determine how tea quality changes with age. The most recently harvested leaves (T1) exhibited superior quality, whereas the older leaves (T4) were suboptimal for brewing purposes. It includes raw, unannotated images that capture the natural diversity of tea leaves, precisely annotated versions for targeted analysis, and augmented data to facilitate advanced research. The compilation process involved extensive on-ground data collection and expert consultations to ensure the authenticity and applicability of the dataset. The "Tea Leaf Age Quality" dataset is a crucial tool for advancing deep learning models in tea leaf classification and quality assessment, ultimately contributing to the technological evolution of the agricultural sector by providing detailed age-stratified tea leaf categorization.

Keywords: Deep learning; Image Processing in Agriculture; Image annotation; Machine Learning in Agriculture; Quality prediction; Tea leaf classification.