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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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923008429 |
| _version_ | 1797429956343496704 |
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| author | Akshatha Prabhu N. Shobha Rani |
| author_facet | Akshatha Prabhu N. Shobha Rani |
| author_sort | Akshatha Prabhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T09:20:34Z |
| format | Article |
| id | doaj.art-0a448f66279e44beb932bbd57a510d11 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-09T09:20:34Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-0a448f66279e44beb932bbd57a510d112023-12-02T07:00:24ZengElsevierData in Brief2352-34092023-12-0151109778AMDPWE: Alphonso Mango Dataset for Precision Weight EstimationAkshatha Prabhu0N. Shobha Rani1Department of Computer Science, School of Computing, Mysuru Campus, Amrita Vishwa Vidyapeetham, IndiaCorresponding author.; Department of Computer Science, School of Computing, Mysuru Campus, Amrita Vishwa Vidyapeetham, IndiaAlphonso 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.http://www.sciencedirect.com/science/article/pii/S2352340923008429Alphonso mangoesMass estimationComputer visionMango processingSustainable technology |
| spellingShingle | Akshatha Prabhu N. Shobha Rani AMDPWE: Alphonso Mango Dataset for Precision Weight Estimation Data in Brief Alphonso mangoes Mass estimation Computer vision Mango processing Sustainable technology |
| title | AMDPWE: Alphonso Mango Dataset for Precision Weight Estimation |
| title_full | AMDPWE: Alphonso Mango Dataset for Precision Weight Estimation |
| title_fullStr | AMDPWE: Alphonso Mango Dataset for Precision Weight Estimation |
| title_full_unstemmed | AMDPWE: Alphonso Mango Dataset for Precision Weight Estimation |
| title_short | AMDPWE: Alphonso Mango Dataset for Precision Weight Estimation |
| title_sort | amdpwe alphonso mango dataset for precision weight estimation |
| topic | Alphonso mangoes Mass estimation Computer vision Mango processing Sustainable technology |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923008429 |
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