An active contour computer algorithm for the classification of cucumbers

Clement, J.; Novas, Nuria; Gázquez, José Antonio; Manzano‐Agugliaro, Francisco

doi:10.1016/j.compag.2013.01.006

Cited by 40 publications

(16 citation statements)

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“…The mean diameters proportioned by the algorithm (72.15 ± 22.62 mm) did not present significant differences (p = 0.35) with the ones measured on fruits (72.68 ± 5.728 mm). Even though the use of 3D LiDAR scans is time-consuming, the harvest size estimation obtained in this research is very accurate.In the literature, diverse works of detection in different types of fruits or harvest can be found, such as almond [2], apple [3][4][5][6][7], cherryfruit [8], cucumber [9], mango [10,11], orange [12,13], pineapple [14,15], or tomato [16].Fruit detection requires segmentation, shape selection, and identification phases [17]. Segmentation consists of filtering through a colour threshold of the components of the scene that can be considered fruit [18].…”

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In-Field Estimation of Orange Number and Size by 3D Laser Scanning

et al. 2019

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The estimation of fruit load of an orchard prior to harvest is useful for planning harvest logistics and trading decisions. The manual fruit counting and the determination of the harvesting capacity of the field results are expensive and time-consuming. The automatic counting of fruits and their geometry characterization with 3D LiDAR models can be an interesting alternative. Field research has been conducted in the province of Cordoba (Southern Spain) on 24 'Salustiana' variety orange trees-Citrus sinensis (L.) Osbeck-(12 were pruned and 12 unpruned). Harvest size and the number of each fruit were registered. Likewise, the unitary weight of the fruits and their diameter were determined (N = 160). The orange trees were also modelled with 3D LiDAR with colour capture for their subsequent segmentation and fruit detection by using a K-means algorithm. In the case of pruned trees, a significant regression was obtained between the real and modelled fruit number (R 2 = 0.63, p = 0.01). The opposite case occurred in the unpruned ones (p = 0.18) due to a leaf occlusion problem. The mean diameters proportioned by the algorithm (72.15 ± 22.62 mm) did not present significant differences (p = 0.35) with the ones measured on fruits (72.68 ± 5.728 mm). Even though the use of 3D LiDAR scans is time-consuming, the harvest size estimation obtained in this research is very accurate.In the literature, diverse works of detection in different types of fruits or harvest can be found, such as almond [2], apple [3][4][5][6][7], cherryfruit [8], cucumber [9], mango [10,11], orange [12,13], pineapple [14,15], or tomato [16].Fruit detection requires segmentation, shape selection, and identification phases [17]. Segmentation consists of filtering through a colour threshold of the components of the scene that can be considered fruit [18]. Different characteristics of perimeter, area, or compaction allow to select the shapes (blobs) and to identify the fruits one by one.In bidimensional models, circles can be detected by the Hough transform [19] or by adjusting circular contours [20]. The colour cameras allowed Harrell et al. [12] to implement a robotic orange harvesting system and Grasso and Recce [21] to perform RGB segmentation. Qureshi et al.[11] used clustering of K-nearest neighbour pixels. Qureshi et al. [22] presented a texture-based method for shape recognition using an over-segmentation of super-pixels from the local gradient calculation.Colour segmentation using charge coupled device cameras returns pixels with RGB graduations or colour composition by addition of the primary colours red, green, and blue, allowing rapid detection of ripe fruit [23]. However, its drawback is its false positives. In addition, shape detection has a high computational cost. Therefore, a suitable alternative is the use of a colour filter followed by shape detection to avoid these false positives.The colour and shape characteristics allow us to approach its count using 2D photos, filtering by colour or chromaticity, delimiting shapes by contour, and being...

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“…Fruit detection requires segmentation, shape selection, and identification phases [17]. Segmentation consists of filtering through a colour threshold of the components of the scene that can be considered fruit [18].…”

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confidence: 99%

In-Field Estimation of Orange Number and Size by 3D Laser Scanning

et al. 2019

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“…Comparatively, our investigation showed an improved accuracy of 92.65% with the use of DWT features. Most of the works, such as Teimouri et al (2014), Font et al (2014, Clement et al (2013), Jamil et al (2009), Omid et al (2010, and Rocha et al (2010) considered only spatial domain features for the purpose of qualification of the agricultural produce. However, there is a large scope for analyzing the images of the agricultural produce under multiresolution processing, which was lacking in the previous works.…”

Section: Discussionmentioning

confidence: 99%

“…Nectarine variety was investigated and verified by Font et al (2014) with an accuracy of 87%. Cucumbers were classified as per the European Grading Standards by Clement et al (2013) with 99% accuracy. Narrow and broad weed were classified based on DWT features by Ghazali et al (2007) with an accuracy of 87.25%.…”

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confidence: 99%

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Kumar¹,

Rajpurohit²,

Jirage³

2018

Journal of Horticultural Research

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Quality assessment is an important concern in the post-harvest marketing of fruits. Manual quality assessment of pomegranate fruits poses various problems because of human operators. In the present paper, an efficient machine vision system is designed and implemented in order to assess the quality of pomegranate fruits. The main objectives of the present study are (1) to adopt a best pre-processing module, (2) to select best class of features and (3) to develop an efficient machine learning technique for quality assessment of pomegranates. The sample images of pomegranate fruits are captured using a custom-made image acquisition system. Two sets of features, namely, spatial domain feature set and wavelet feature set are extracted for all of the sample images. Experiments are conducted by training both artificial neural networks (ANNs) and support vector machines (SVMs) using both sets of features. The results of the experiments illustrated that ANNs outperform SVMs with a difference in the accuracy of 12.65%. Further, the selection of wavelet featureset for training yielded more accurate results against spatial domain feature set.Key words: Pomegranate quality assessment, wavelet features for fruit quality analysis, machine intelligence in pomegranate quality assessment, ANN for fruit quality assessment

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“…For consumers, food quality is defined according to the visual and nutritional characteristic, particularly in the case vegetables, where acceptance is based on general appearance, color, brightness, odor, signs of wilting, texture and nutritional factors (Butz et al, 2005;Clement et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Influence of refrigeration system parameters during storage of Lactuca Sativa l under non-isothermal conditions

Schmitz

Hoffmann

Angioletti

et al. 2020

BJD

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Lettuce (Lactuca sativa L.) is a leaf vegetable rich in vitamins, minerals, fiber and bioactive compounds. Its preservation after harvest usually occurs under refrigeration, slowing the chemical and microbiological reactions. The objective of this study was to verify the influence of the refrigeration parameters (set temperature and oscillation range) on the preservation of hydroponic crispy lettuce (Lactuca sativa L.), variety Solaris, in terms of weight loss and moisture, vitamin C and total chlorophyll contents. Samples were analyzed during 14 days of storage packed in expanded polystyrene (EPS) packaging wrapped with polyvinyl chloride (PVC). Different conditions of set temperature (2 °C, 6 °C and 10 °C) and oscillation range (±1 °C, ±3 °C and ±5 °C) were tested, using a 3² experimental design. The analysis revealed losses in the mass and moisture content after 14 days, with the greatest reductions being observed for the treatments with a set temperature of 10 °C and all oscillation ranges (±1 °C, ±3 °C and ±5 °C). However, the vitamin C and total chlorophyll contents showed variations during storage for all treatments studied. Based on the results of this study, it was concluded that applying a low temperature (2 °C), with all 3 oscillation ranges studied, can prolong the lettuce shelf life.

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An active contour computer algorithm for the classification of cucumbers

Cited by 40 publications

References 21 publications

In-Field Estimation of Orange Number and Size by 3D Laser Scanning

In-Field Estimation of Orange Number and Size by 3D Laser Scanning

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Influence of refrigeration system parameters during storage of Lactuca Sativa l under non-isothermal conditions

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