RipSetCocoaCNCH12: Labeled Dataset for Ripeness Stage Detection, Semantic and Instance Segmentation of Cocoa Pods

Juan Felipe Restrepo-Arias (), María Isabel Salinas-Agudelo, María Isabel Hernandez-Pérez, Alejandro Marulanda-Tobón and María Camila Giraldo-Carvajal
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Juan Felipe Restrepo-Arias: Escuela de Ciencias Aplicadas e Ingeniería, Universidad EAFIT, Medellín 050022, Colombia
María Isabel Salinas-Agudelo: Escuela de Ciencias Aplicadas e Ingeniería, Universidad EAFIT, Medellín 050022, Colombia
María Isabel Hernandez-Pérez: Escuela de Ciencias Aplicadas e Ingeniería, Universidad EAFIT, Medellín 050022, Colombia
Alejandro Marulanda-Tobón: Escuela de Ciencias Aplicadas e Ingeniería, Universidad EAFIT, Medellín 050022, Colombia
María Camila Giraldo-Carvajal: Escuela de Ciencias Aplicadas e Ingeniería, Universidad EAFIT, Medellín 050022, Colombia

Data, 2023, vol. 8, issue 6, 1-10

Abstract: Fruit counting and ripeness detection are computer vision applications that have gained strength in recent years due to the advancement of new algorithms, especially those based on artificial neural networks (ANNs), better known as deep learning. In agriculture, those algorithms capable of fruit counting, including information about their ripeness, are mainly applied to make production forecasts or plan different activities such as fertilization or crop harvest. This paper presents the RipSetCocoaCNCH12 dataset of cocoa pods labeled at four different ripeness stages: stage 1 (0–2 months), stage 2 (2–4 months), stage 3 (4–6 months), and harvest stage (>6 months). An additional class was also included for pods aborted by plants in the early stage of development. A total of 4116 images were labeled to train algorithms that mainly perform semantic and instance segmentation. The labeling was carried out with CVAT (Computer Vision Annotation Tool). The dataset, therefore, includes labeling in two formats: COCO 1.0 and segmentation mask 1.1. The images were taken with different mobile devices (smartphones), in field conditions, during the harvest season at different times of the day, which could allow the algorithms to be trained with data that includes many variations in lighting, colors, textures, and sizes of the cocoa pods. As far as we know, this is the first openly available dataset for cocoa pod detection with semantic segmentation for five classes, 4116 images, and 7917 instances, comprising RGB images and two different formats for labels. With the publication of this dataset, we expect that researchers in smart farming, especially in cocoa cultivation, can benefit from the quantity and variety of images it contains.

Keywords: cocoa pods detection; ripeness stage detection; semantic segmentation; smart farming (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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