Computer vision-based apple grading for golden delicious apples based on surface features

Moallem, Payman; Serajoddin, Alireza; Pourghassem, Hossein

doi:10.1016/j.inpa.2016.10.003

Cited by 115 publications

(65 citation statements)

References 12 publications

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“…The used of ANN in the classification analysis of agrobased product as well as in other application has been widely used such as in the classification of Chinese rice seed varieties [11], apple grading [12], discrimination of rapeseed varieties [13], olive fruits recognition [14], detection of segmentation points of Arabic Words [15] and face recognition [16].…”

Section: Introductionmentioning

confidence: 99%

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Aznan

Ruslan

Rukunudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Abstract-Determination of rice seed varieties is very important to ensure varietal purity in the production of high-quality seed. To date, manual seed inspection is carried out to separate foreign rice seed varieties in rice seed sample in the laboratory as there is lack of an automatic seed classification system. This paper describes a simple approach of using image processing technique and artificial neural network (ANN) to determine rice seed varieties based on extracted colour features of individual seed images. The experiment was conducted using 200 individual seed images of two Malaysian rice seed varieties namely MR 219 and MR 269. The acquired seed images were processed using a set of image processing procedure to enhance the image quality. Colour feature extraction was carried out to extract the red (R), green (G), blue (B), hue (H), saturation (S), value (V) and intensity (I) levels of the individual seed images. The classification using ANN was carried out by dividing the data sets into training (70% of data), validation (15%) and testing (15%) dataset respectively. The best ANN model to determine the rice seed varieties was developed, and the accuracy levels of the classification results were 67.5% and 76.7% for testing and training data sets using 40 hidden neurons.

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Section: Introductionmentioning

confidence: 99%

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Aznan

Ruslan

Rukunudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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“…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. A kiwifruit detection system using semantic segmentation was able to detect 76.0% of kiwifruit in a real‐world orchard (H. A. Williams et al, ).…”

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. A kiwifruit detection system using semantic segmentation was able to detect 76.0% of kiwifruit in a real-world orchard (Williams et al, 2018), showing promise for the detection of the flowers under similar 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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Computer vision-based apple grading for golden delicious apples based on surface features

Cited by 115 publications

References 12 publications

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

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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