Detection of passion fruits and maturity classification using Red-Green-Blue Depth images

Tu, Shuqin; Xue, Yong; Zheng, Changqing; Qi, Yu; Hua, Wei; Mao, Liang

doi:10.1016/j.biosystemseng.2018.09.004

Cited by 112 publications

(45 citation statements)

References 17 publications

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“…ShuqinTu et al [9] developed a method for detecting different growth stages of the passion fruits and for identification of maturity using natural outdoor RGB-D images. Passion fruits on the same branch have different maturity stages.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

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A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

Mohan¹,

Sreekumar²

2019

IJCA

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Plump fruits are a vital part of the human diet giving mandatory vitamins, minerals and other health encouraging compounds. Quality assessment and finding of fruit ripeness is a major concern in agriculture business and becomes a growing research concern in computer vision. Image processing is an advanced field which led to a higher demand to reduce the high rate of errors and given more possible results. Therefore, the objective of many types of research is to standardize and reduce manual work in the classification of tomatoes ripeness. One of the most important feature of an image is color. Estimating the ripeness of fruits via color can be performed as it is the dominant feature in describing the information of the image. However, each color models have been given a different performance when used in the experiment. This paper is a survey of different techniques that are deployed over different varieties of fruit images in order to detect maturity stages for ripening, fruit region estimation and also, the effect of different color models and other features on detecting ripeness was studied in this literature survey.

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Section: Review Of Existing Methodsmentioning

confidence: 99%

“…By using these features they got better performance. 9 Tu, Shuqin, et al [9] Faster region based convolution neural In order to get better result they also captured the depth of the image and got a better result networks (Faster R-CNN) used for detection.…”

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confidence: 99%

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

Mohan¹,

Sreekumar²

2019

IJCA

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“…Based on Table 3 the best colour reading on tomato samples is in the repetition I and III, which identified the ripe and half-ripe fruit with the percentage of colour uniformity and average accuracy of 80%. Therefore, the percentage of light that was entering the sensor room was low, which indicates that the sensor will read the original colour if the reading process does not disturb by the light [5]. On the other hand,the repetition II shows a lower accuracy and uniformity percentage of 78.3%.…”

Section: Overall System Testingmentioning

confidence: 99%

“…So that the researcher took 2 different points while performance testing, namely in the middle and near the sensor. Sensor reading distance must not exceed 2 cm because it will be difficult to recognize the colour of the object [5]. Moreover, the reading result from the sensor will be stored in the EEPROM and then processed by the microcontroller, then the information from the microcontroller will be read by the LCD to display the reading reasult data.…”

Section: Research Proceduresmentioning

confidence: 99%

Application of Color Sensor in the Determination of Tomato Fruit Ripeness (Solanum Lycopersicum, L) in Gravitation Type Fruit Sorting Tool (Gravitation Type)

Putra

Rizaldi

2019

InJAR

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Sortation is an important step in handling post-harvest fruits to extend the shelf life and increase the selling value. A tool that is able to sort quickly is needed to speed up the sortation process. Therefore, design of fruit sortationtool using gravitation type and technology to determine fruit ripeness is needed. This study aimed to design and fabricate fruit sorting tool based on the fruit size that could help farmers in sorting fruit. The performance testing was conducted using tomatoes. The results of the design shows that this tool could accommodate 12 kg (equal to 60 tomatoes) of fruit. Tool performance testing shows that this tool could work effectively on a slope of 12˚ withan effective capacity of 133.1 kg/hour (80%). Damage analysis showed that the ripe fruit was more susceptible to damage than the half-ripe fruit due to the fact that half-ripe fruit was still hard, so that the percentage of the damage analysis in ripe fruit was 23.3% and half-ripewas 10%.

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“…In orchards, fruits have been detected by image analysis. For example, Tu et al [ 22 ] were able to detect passion fruit and classify ripeness using a neural network from RGB-D images, with an accuracy of 92.71 and 91.52%, respectively. Guava fruits were detected using RGB-D images to enable automatic harvesting, without touching branches, using a robot [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner

Ohashi

Ishigami

Gotō

2020

Sensors

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Monitoring the growth of fruit vegetables is essential for the automation of cultivation management, and harvest. The objective of this study is to demonstrate that the current sensor technology can monitor the growth and yield of fruit vegetables such as tomato, cucumber, and paprika. We estimated leaf area, leaf area index (LAI), and plant height using coordinates of polygon vertices from plant and canopy surface models constructed using a three-dimensional (3D) scanner. A significant correlation was observed between the measured and estimated leaf area, LAI, and plant height (R2 > 0.8, except for tomato LAI). The canopy structure of each fruit vegetable was predicted by integrating the estimated leaf area at each height of the canopy surface models. A linear relationship was observed between the measured total leaf area and the total dry weight of each fruit vegetable; thus, the dry weight of the plant can be predicted using the estimated leaf area. The fruit weights of tomato and paprika were estimated using the fruit solid model constructed by the fruit point cloud data extracted using the RGB value. A significant correlation was observed between the measured and estimated fruit weights (tomato: R2 = 0.739, paprika: R2 = 0.888). Therefore, it was possible to estimate the growth parameters (leaf area, plant height, canopy structure, and yield) of different fruit vegetables non-destructively using a 3D scanner.

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Detection of passion fruits and maturity classification using Red-Green-Blue Depth images

Cited by 112 publications

References 17 publications

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

Application of Color Sensor in the Determination of Tomato Fruit Ripeness (Solanum Lycopersicum, L) in Gravitation Type Fruit Sorting Tool (Gravitation Type)

Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner

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