Work

A multi-stage dataset for banana bunch detection and harvesting decision support

Year: 2025

Type: data-paper

Abstract: This study introduces a multi-part dataset designed to support the development of artificial intelligence models for banana bunch detection and harvesting decision-making. The dataset includes images collected from four different fields in Madeira Island, Portugal, under varying environmental conditions. It is divided into three core subsets, namely, a detection dataset annotated using You Only Look Once (YOLO) format (2179 images labelled for bunch and flower bud detection), a harvesting classification dataset labelled by expert teams as "Cut"" and "Keep" (2685 images, with 1143 labelled as "Cut" and 1542 as "Keep") and an expert opinion dataset where images were classified by human experts into three decision categories: "Cut now", "Keep for next cut" and "Wait more than three weeks" (400 images, with 100 samples evaluated by each of four expert cutters, capturing all three decision categories) These datasets enable the creation and benchmarking of computer vision models and allow for expert consensus analysis.

Source: Data in Brief

DOI: 10.1016/j.dib.2025.112337

Authors: Preety Baglat, Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias

Institutions: Instituto de Tecnologias Interativas, Universidade da Madeira

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,220