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AMDPWE: Alphonso Mango Dataset for Precision Weight Estimation

Alphonso Mango (Mangifera indica L.), is popularly known as king of mangoes in India. India is one of the leading countries in mango production. Automatic visual inspection systems for quality assessment using weight are intelligent interventions designed to evaluate fruit maturity based on various...

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Detalles Bibliográficos
Autores principales: Prabhu, Akshatha, Rani, N. Shobha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694035/
http://dx.doi.org/10.1016/j.dib.2023.109778
Descripción
Sumario:Alphonso Mango (Mangifera indica L.), is popularly known as king of mangoes in India. India is one of the leading countries in mango production. Automatic visual inspection systems for quality assessment using weight are intelligent interventions designed to evaluate fruit maturity based on various parameters. Automated systems utilize a combination of image analysis, computer vision, and artificial intelligence algorithms to estimate the weight of fruits precisely. One of the crucial quality parameters is weight, which measures the fruit's overall mass and potential quality. Integration of precision weighing mechanisms in fruit quality estimation leads to a quick and accurate method of measuring fruit weight in the marketplace. Furthermore, the fruit's demand in the market is directly connected to its size as it influences consumer preferences. Automatic precision weight estimation systems equipped with intelligent high-resolution assists in ensuring consistency in size across batches of fruits. The dataset samples consist of images of 71 Alphonso cultivars of mango fruit. The fruit is collected from the College of Horticulture Yalachahalli, Mysuru, India. The fruits were harvested in April/May 2022. The digital images of these fruits are captured using the acquisition setup with a controlled environment. Each image has a resolution of 2048×1536. The images include two orientations of each sample. The physical parameters such as the weight, fruit diameter, and width across the shoulder are also maintained. The digital images undergo pre-processing, and further, the vision-based features such as area, convex area, and minor axis for both orientations are captured.