A methodology for fresh tomato maturity detection using computer vision

Wan, Pengfei; Toudeshki, Arash; Tan, Huifeng; Ehsani, Reza

doi:10.1016/j.compag.2018.01.011

Cited by 173 publications

(111 citation statements)

References 29 publications

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“…Peng Wan et al [10] developed a color analysis method for calculating the feature color values for detecting the maturity levels (green, orange and red) of fresh market tomatoes and then use backpropagation neural network (BPNN) classification technique to sort tomatoes based on the color features with potential future application in in-field yield estimation. With the help of computer vision technology, 200 samples per variety [Roma, Pear] was chopped randomly based on maturity in the lab.…”

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%

“…even though they used a large amount of sample images. 10 Wan, Peng, et al [10] For segmentation algorithm such as threshold segmentation, noise cancellation, image contour extraction and boundary fill algorithm were used.…”

mentioning

confidence: 99%

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

Mohan¹,

Sreekumar²

2019

IJCA

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“…Soft computing approaches have become the de facto approach for agricultural robotics as they have been found to be able to handle dynamic conditions reliably (Y. Huang et al, ). They have been utilised for a wide range of agricultural tasks: harvesting, yield estimation, weed‐spraying, pollination, and crop management (Bargoti & Underwood, a, b; Dias, Tabb, & Medeiros, ; Kurosaki et al, ; Nachtigall, Araujo, & Nachtigall, ; Sa et al, ; Wan, Toudeshki, Tan, & Ehsani, ; Wang, Song, & He, ; Zhang et al, ). Detection of apples (Bargoti & Underwood, b; Dias et al, ; Inthiyaz, Kishore, & Madhav, ; Moallem, Serajoddin, & Pourghassem, ; Prasad et al, ; Puttemans, Vanbrabant, Tits, & Goedemé, ; Soleimani Pour, Chegini, Zarafshan, & Massah, ) and strawberries (Habaragamuwa et al, ; Puttemans et al, ) has shown good results with detection rates up to 90% of the fruit under real‐world orchard conditions.…”

Section: Related Workmentioning

confidence: 99%

Improvements to and large‐scale evaluation of a robotic kiwifruit harvester

Williams

Ting

Nejati

et al. 2019

Journal of Field Robotics

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The growing popularity of kiwifruit orchards in New Zealand is increasing the already high demand for seasonal labourers. A novel robotic kiwifruit harvester has been designed and built to help meet this demand [H. A. Williams et al. Biosystems Eng. 181 (2019), pp. 140–156]. Early evaluations of the platform have shown good results with the system capable of harvesting 51.0% of 1,456 kiwifruit in four bays with an average cycle‐time of 5.5 s/fruit. However, the harvester's high cycle‐time, and high fruit loss rate at 23.4%, prevent it from being commercially viable. This paper presents two new developments for the harvesting system, a new vision system and two new gripper variations designed to overcome the harvester's previous limitations. Furthermore, we have designed and conducted the largest real‐world evaluation of a robotic fruit harvesting system published to date with over 12,000 kiwifruit involved. The results of this trial demonstrated that our kiwifruit harvester is one of the most effective selective fruit harvesters in the world capable of successfully harvesting 86.0% of reachable fruit, and 55.8% of all kiwifruit with a cycle‐time of 2.78 s/fruit.

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“…Soft computing techniques have been utilized for a wide range of harvesting, yield estimation, weed-spraying, pollination, and crop management within orchards (Bargoti & Underwood, 2017a, 2017bDias, Tabb, & Medeiros, 2018;Kurosaki et al, 2011;Nachtigall, Araujo, & Nachtigall, 2016;Sa et al, 2016;Wang, Song, & He, 2017;Wan, Toudeshki, Tan, & Ehsani, 2018;Zhang et al, 2017). Detection of Apples (Bargoti & Underwood, 2017b;Dias et al, 2018;Inthiyaz, Kishore, & Madhav, 2018;Moallem, Serajoddin, & Pourghassem, 2017;Prasad et al, 2018;Puttemans, Vanbrabant, Tits, & Goedemé, 2017;Soleimani Pour, Chegini, Zarafshan, & Massah, 2018) and strawberries (Habaragamuwa et al, 2018;Puttemans et al, 2017) have shown good results with detection rates up to 90% of the fruit under real-world orchard conditions.…”

Section: Fruit and Flower Detectionmentioning

confidence: 99%

Autonomous pollination of individual kiwifruit flowers: Toward a robotic kiwifruit pollinator

Williams

Nejati

Hussein

et al. 2019

Journal of Field Robotics

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There is an increasing concern that the traditional approach of natural kiwifruit pollination by bees may not be sustainable. The alternatives are currently too costly for most growers due to high labor requirements or inefficient usage of expensive pollen. This paper presents a performance evaluation of a novel kiwifruit pollinating robot designed to provide a more efficient, reliable, and cost‐effective means of producing kiwifruit. The robot comprises a novel air‐assisted sprayer, a machine vision system employing convolution neural networks, and a flower targeting system for efficient and effective application of pollen to individual flowers. We show that this pollination system is capable of individually targeting and pollinating 79.5% of flowers at 3.5 km/hr while using comparable amounts of pollen to commercial Cambrian operators. Furthermore, flowers that were successfully pollinated at 1 km/hr grew into the first robotically pollinated kiwifruit which were comparable in quality to commercially grown kiwifruit. However, the overall fruit set was found to be well below commercial requirements and further work on increasing the overall yield is required.

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A methodology for fresh tomato maturity detection using computer vision

Cited by 173 publications

References 29 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

Improvements to and large‐scale evaluation of a robotic kiwifruit harvester

Autonomous pollination of individual kiwifruit flowers: Toward a robotic kiwifruit pollinator

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