Search for Optimum Color Space for the Recognition of Oranges in Agricultural Fields

Hernández-Hernández, José Luis; Hernández-Hernández, Mario; Feliciano-Morales, Severino; Álvarez-Hilario, Valentín; Herrera-Miranda, Israel

doi:10.1007/978-3-319-67283-0_22

Cited by 9 publications

(13 citation statements)

References 12 publications

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“…It is worth noting that agriculture-oriented food recognition is more similar to object recognition in the computer vision, such as fruit recognition [Hernandez-Hernandez et al 2017] and egg classification [Patel et al 1998]. However, it is quite different from dish or ingredient recognition.…”

Section: Agriculturementioning

confidence: 99%

“…It will also play an important role in the smart farming. Existing works such as [Chen et al 2017c;Hernandez-Hernandez et al 2017] can recognize and count the fruits in the trees. More and more food computing systems will be applied to help detect the illness of the food to guarantee the food safety and quantity.…”

Section: Other Promising Applications In the Vertical Industrymentioning

confidence: 99%

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A Survey on Food Computing

et al. 2019

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Food is very essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding the human behavior, improving the human health and understanding the culinary culture.With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from disparate sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food related issues in medicine, biology, gastronomy and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, vast amounts of work has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews, which shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this paper, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.

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Section: Agriculturementioning

confidence: 99%

Section: Other Promising Applications In the Vertical Industrymentioning

confidence: 99%

A Survey on Food Computing

et al. 2019

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“…YCbCr color model commonly use in operation in digital video and image compression [21]. High accuracy has been achieved in classification of ripeness stage for watermelon [19] and orange [22] but moderate for tomato [20] ripeness classification for a least noise background environment. In orange ripeness recognition, lowest error recognition with the most robust algorithm for orange recognition has been recorded [22].…”

Section: Introductionmentioning

confidence: 99%

“…The I1I2I3 color model used in apple detection where it successfully distinguish object form background [23]. In orange recognition, this color model shows a high recognition error compared to other color model [22]. This color model has also been used to recognize dates.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Color Models for Palm Oil Fresh Fruit Bunch Ripeness Classification

Sabri¹,

İbrahim²,

Isa³

2018

IJEECS

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This paper investigates the application of eight color models for automatic palm oil Fresh Fruit Bunch (FFB) ripeness classification with multi-class Support Vector Machine (SVM). Ripeness classification is important during harvesting to ensure that they are harvested during the correct ripe stage for optimum oil production. Since color is a significant indicator for agriculturists to determine the ripeness of FFB, it is critical to determine the right color model. Eight color models have been investigated namely, HSV, I1I2I3, LAB, XYZ, YCbCr, YIQ, YUV and RGB. Color moments were extracted from each of these color models for the classification of four stages of FFB ripeness that are unripe, under-ripe, ripe and over-ripe. A database of five hundred images of palm oil FFB has been constructed and experiments showed that YCbCr and YUV outperform the other color models.

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