Tomatoes selection system based on fruit maturity level using digital color analysis method

Chandra, Theodore Gautama; Erwandha, Kevin G.; Aditya, Y; Priyantari, Bina A.; Fitriani, Dewi; Hakim, M. Hanifuddin; Hidayat, Achmad Syarief; Hatta, Agus Muhamad; Irawati, Ninik

doi:10.1117/12.2504987

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Image processing and machine vision for the maturity level classification of fruits have been intensively investigated [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Most work to date has focused on maturity analysis of fruit that ripen in a uniform fashion, such as tomato [ 32 , 33 , 34 ], passion fruit [ 27 ], apricot [ 24 ], persimmon [ 35 ], blueberry [ 36 , 37 ], cherry [ 38 ], and date [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

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The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant.

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Section: Introductionmentioning

confidence: 99%

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

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“…The fruit picking time mainly depends on the maturity of the fruit [1][2][3]. Fruits of different usages should be picked at different stages of maturity.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition of Modern Agricultural Fruit Maturity Based on Internet of Things

Chen¹,

Tang²,

Xi³

et al. 2021

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The accurate judgement of fruit maturity is significant for modern agriculture. At present, few scholars have monitored and recognized fruit maturity based on the Internet of things (IoT) and image recognition technology. Therefore, this paper explores the image recognition of fruit maturity in the context of agricultural Internet of things (IoT). Firstly, the single shot multi-box detection (SSD) algorithm was improved for fruit recognition and positioning, and used to determine the size and position the fruits to be recognized. Next, an image fusion algorithm was designed based on improved Laplacian pyramid, which effectively compresses the large fruit monitoring images shot in the same scene. The proposed algorithm was proved feasible and effective through experiments.

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Tomatoes selection system based on fruit maturity level using digital color analysis method

Cited by 2 publications

References 2 publications

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Image Recognition of Modern Agricultural Fruit Maturity Based on Internet of Things

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