Journal articles: 'Fruit imaging'

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Hansen, James D., Donald W. Schlaman, Ron P. Haff, and Wee L. Yee. "Potential Postharvest Use of Radiography to Detect Internal Pests in Deciduous Tree Fruits." Journal of Entomological Science 40, no. 3 (July 1, 2005): 255–62. http://dx.doi.org/10.18474/0749-8004-40.3.255.

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Radiographic techniques were investigated for their potential to detect internal pests in deciduous tree fruits. Two non-destructive methods, X-ray CT imaging and film X-ray, were used to detect larval feeding damage caused by codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae), in apples. In addition, CT imaging was used to detect larvae of the codling moth and western cherry fruit fly, Rhagoletis indifferens Curran (Diptera: Tephritidae), in cherries. Both techniques showed evidence of codling moth feeding tunnels in apples, as well as in cherries using CT imaging. CT images of cherries infested with fruit fly larvae showed retraction of the fruit pulp from the seed. This study supports the use of radiography to detect internally damaged fruits for sorting on the commercial packing line.

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Maas, John L., and M. J. Line. "NUCLEAR MAGNETIC RESONANCE IMAGING OF FUNGAL INFECTIONS IN STRAWBERRY FRUIT." HortScience 30, no. 2 (April 1995): 192b—192. http://dx.doi.org/10.21273/hortsci.30.2.192b.

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We report the use of nuclear magnetic resonance (NMR) imaging to detect differences in invasion and colonization of fruit by pathogens (Botrytis cinerea, Colletotrichum acutatum, and Phytophthora cactorum), and bruise wounds are sharply distinguishable from healthy fruit tissue by their T1 times. Digitized images from T1 images clearly show two or more zones of pathogen activity in fruit tissue. The innermost zone corresponds to the area of greatest invasive activity at the leading margin of the infection. A second zone corresponds to the area of tissue that has been killed and is being degraded by the pathogen. Sometimes, a third zone is present at the outer border of the lesion and this correspond to where aerial sporulation may occur. Images of bruises, however, are uniform with no apparent gradations in T1 characteristics. Detection of fruit deterioration and decay is important in understanding and controlling postharvest loss of fruit crops. The nondestructive nature of MRI provides a means to quantify the process of decay development and control measures applied to fruits.

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Jiang, Ying Lan, Ruo Yu Zhang, Jie Yu, Wan Chao Hu, and Zhang Tao Yin. "Detection of Infected Tephritidae Citrus Fruit Based on Hyperspectral Imaging and Two-Band Ratio Algorithm." Advanced Materials Research 311-313 (August 2011): 1501–4. http://dx.doi.org/10.4028/www.scientific.net/amr.311-313.1501.

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The Oriental fruit fly, Bactrocera dorsalis Hendel, is a serious pest insect for citrus fruits. The infected peel area can cause rot and fruit drop. However, there is no efficient automatic detection technology at this time that could detect the infected fruit. In this investigation, hyperspectral reflectance images were evaluated for detecting infected area on the citrus surface in the wavelength range between 500 and 950 nm. Optimum Index Factor (OIF) method was applied to identify the optimal band combination. Experiments result with a 97.5% recognition rate showed that hyperspectral imaging and proposed classification method were effective in differentiation of infected fruit and normal fruit. This study will lay a foundation for developing multispectral detection system used in on-line detection of infected fruit.

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Garillos-Manliguez, Cinmayii A., and John Y. Chiang. "Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation." Sensors 21, no. 4 (February 11, 2021): 1288. http://dx.doi.org/10.3390/s21041288.

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Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya fruit. Deep learning architectures have gained respect and brought breakthroughs in unimodal processing. This paper suggests a novel non-destructive and multimodal classification using deep convolutional neural networks that estimate fruit maturity by feature concatenation of data acquired from two imaging modes: visible-light and hyperspectral imaging systems. Morphological changes in the sample fruits can be easily measured with RGB images, while spectral signatures that provide high sensitivity and high correlation with the internal properties of fruits can be extracted from hyperspectral images with wavelength range in between 400 nm and 900 nm—factors that must be considered when building a model. This study further modified the architectures: AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 to utilize multimodal data cubes composed of RGB and hyperspectral data for sensitivity analyses. These multimodal variants can achieve up to 0.90 F1 scores and 1.45% top-2 error rate for the classification of six stages. Overall, taking advantage of multimodal input coupled with powerful deep convolutional neural network models can classify fruit maturity even at refined levels of six stages. This indicates that multimodal deep learning architectures and multimodal imaging have great potential for real-time in-field fruit maturity estimation that can help estimate optimal harvest time and other in-field industrial applications.

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Cornelissen, Tom, Patrik Verstreken, and Wim Vandenberghe. "Imaging mitophagy in the fruit fly." Autophagy 14, no. 9 (August 2, 2018): 1656–57. http://dx.doi.org/10.1080/15548627.2018.1496720.

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G., ANNAPOORANI. "A Survey on Application of Hyperspectral Imaging Techniques in Assessing Fruit Quality." Journal of Research on the Lepidoptera 51, no. 2 (May 15, 2020): 303–18. http://dx.doi.org/10.36872/lepi/v51i2/301098.

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Suherly, Tomy, and Minarni Shiddiq. "Estimasi Volume Buah Kiwi Menggunakan Metode Pencitraan dan Aturan Simpson." JURNAL MEDIA INFORMATIKA BUDIDARMA 4, no. 3 (July 20, 2020): 535. http://dx.doi.org/10.30865/mib.v4i3.2144.

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Volume is one of important quantities that have been applied to fruit sorting based on size. Imaging method or computer vision is a simple non destructive method that has been proposed to measure fruits volume. This study was aimed to estimate the volumes of kiwi fruits using Computer Vision imaging method and compared to a water displacement method. The samples were 20 green kiwi fruits (Actinidia deliciosa). A smartphone camera was used to record the kiwifruit images and Python based program to drive the camera and process the images. Images resulted in Computer Vision are two dimensions (2D) images. The 1/3 rd Simpson rule was employed to determine the volume of kiwi fruits based on the volume integration of a spinning object where surface image of kiwi was divided into 8 parts and then summed. The results show that the 2D imaging method assisted by the Simpson rule was successfully able to determine the kiwi fruit volumes with 4.57 % average difference percentage compared to the water displacement method. This was about 4.97 cm3 of average volume difference of 20 samples. The sample volumes measured using this method ranges from 82,48 cm3 - 126,85 cm3. These results will be one of steps toward the development of machine vision for fruit sorter based on volume

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Lin, W. C., J. W. Hall, and A. Klieber. "Video Imaging for Quantifying Cucumber Fruit Color." HortTechnology 3, no. 4 (October 1993): 436–39. http://dx.doi.org/10.21273/horttech.3.4.436.

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A video-imaging technique, using commercial software to process images obtained at 550 nm, was established to estimate chlorophyll content of cucumber fruit disks. The chlorophyll content of excised disks was extracted, determined, and regressed on the video-image grey level. They were linearly related. The change in grey level of the whole visible image accurately indicated the change of green color during fruit development on the vine and the loss of green color after 1 week of storage at 13C. The relationship of the chlorophyll content on grey level was quadratic for three imaging methods: 1) average grey level of the five disks; 2) average grey level of the whole cucumber image; and 3) average grey level of central one-third of the whole cucumber image. Chlorophyll content was most highly correlated to the grey level of the disks themselves (residual SD = 6.74 μg·cm-2), but this sampling technique was destructive. Both one-third of the fruit image (SD = 9.25 μg·cm-2) and the whole image (SD = 9.36 μg·cm-2) provided satisfactory precision. For simplicity, whole-fruit imaging is suitable for estimating fruit chlorophyll content and for quantifying fruit green color intensity. Potential use of this technique in product sorting and shelf life prediction of long English cucumbers is discussed.

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Kurita, Keisuke, Yuta Miyoshi, Yuto Nagao, Mitsutaka Yamaguchi, Nobuo Suzui, Yong-Gen Yin, Satomi Ishii, et al. "Fruit PET: 3-D imaging of carbon distribution in fruit using OpenPET." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 954 (February 2020): 161843. http://dx.doi.org/10.1016/j.nima.2019.01.069.

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Abbas, Amel H., and Marwa A. Shamel. "Identify and Classify Normal and Defects of Prunus_armeniaca Using Imaging Techniques." Kurdistan Journal of Applied Research 2, no. 3 (August 27, 2017): 1–6. http://dx.doi.org/10.24017/science.2017.3.11.

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The Prunus_armeniaca fruit is classified manually in wholesale markets, supermarkets and food processing plants on a normal or defects basis. The aim of this research is to replace the manual sorting techniques using computer vision techniques and applications by proposing techniques for identify and recognitions patterns through the use of 150 fruits of Prunus_armeniaca, 10 for the testing stage in fresh and 10 for testing stage in case of defects. The fruits Prunus_armeniaca collected from growing trees in the large fields of Salah al-Din province\Iraq. The system designed for classification based on the color image taken inside a black box used camera pixel resolution of (13 mega) with a constant intensity of light. . Used K-mean in phase segmentations and only computed 13 features derive statistics from GLCM .classification phase used SVM classify fruit into two class, either (normal or defects) .Results the system success rate reach 100%.The work done using MATLAB R2016a.

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He, Jian Guo, Yang Luo, Gui Shan Liu, Shuang Xu, Zhen Hua Si, Xiao Guang He, and Song Lei Wang. "Prediction of Soluble Solids Content of Jujube Fruit Using Hyperspectral Reflectance Imaging." Advanced Materials Research 706-708 (June 2013): 201–4. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.201.

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To predict soluble solids content (SSC) of jujube fruits, a hyperspectral imaging technique has been used for acquiring reflectance images from 200 samples in the spectral regions of 900-1700nm. Hyperspectral images of jujubes were evaluated from the regions of interest using principal component analysis (PCA) with the goal of selecting five optimal wavelengths (1034, 1109，1231，1291 and 1461nm). Prediction model of SSC (Rp=0.9027, RMSEP=1.9845) were built based on BP neural network. This research has demonstrated the feasibility of implementing hyperspectral imaging technique for sorting jujube fruit for SSC to enhance the product quality and marketability.

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Paddock, Stephen W. "Confocal Imaging of Scale Development on Butterfly Wings." Microscopy and Microanalysis 7, S2 (August 2001): 1022–23. http://dx.doi.org/10.1017/s1431927600031184.

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Both surfaces of the butterfly wing are covered with thousands of colored scales that are arranged in precise linear arrays. Each scale contributes a single color to the overall pattern, which is usually different on the upper and lower surface of the wing. Moreover, the scales have a characteristic surface substructure that is visible using confocal autofluorescence (fig. 1). The ridges on the surface of all of the scales function to control the flow of air across the wing surface during gliding flight, and they contribute to the interference colors in a subset of the scales.The fruit fly Drosophila melanogaster is the closest relative of butterflies for which detailed developmental, genetic and confocal imaging protocols have been established. Specimen preparation techniques for butterfly imaginal discs were largely developed from those for Drosophila tissues. The various stages of butterfly wing development have presented new challenges for confocal imaging. For example, butterfly fifth instar wing imaginal discs are much larger and thicker than those of the fruit fly.

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Baek, Seunghoon, Jongguk Lim, Jun Gu Lee, Michael J. McCarthy, and Seong Min Kim. "Investigation of the Maturity Changes of Cherry Tomato Using Magnetic Resonance Imaging." Applied Sciences 10, no. 15 (July 28, 2020): 5188. http://dx.doi.org/10.3390/app10155188.

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The maturity of tomato fruit is normally characterized by external color, and it is often difficult to know when fruit have achieved commercial maturity or become over-mature. The internal structure of tomato fruit changes during development and this study investigates the utility of nondestructive measurement of tomato fruit structure as a function of maturity using magnetic resonance imaging (MRI). The objective of this work is to use analysis of internal tomato fruit structural measurements to characterize maturity. Intact cherry tomato fruit were harvested at six different maturity stages. At each stage of maturity, the internal structure of the fruit was measured using a series of two-dimensional (2D) magnetic resonance (MR) images. Qualitative and quantitative image analyses were performed to correlate internal fruit structure with maturity. Internal structural changes observed in the pericarp region of the tomato fruit are highly correlated with fruit maturity. MR image information combined with classical analysis techniques provides a more complete understanding of structure and physicochemical changes in tomato fruit during maturation. This study demonstrates that MRI is a useful analytical tool to characterize internal changes in agricultural produce as the produce matures. This technique can be applied to almost any agricultural produce to monitor internal physical changes due to external impact, maturity stage, variation in climate, storage time, and condition, or other factors impacting quality.

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Feng, Juan, Lihua Zeng, and Long He. "Apple Fruit Recognition Algorithm Based on Multi-Spectral Dynamic Image Analysis." Sensors 19, no. 4 (February 23, 2019): 949. http://dx.doi.org/10.3390/s19040949.

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The ability to accurately recognize fruit on trees is a critical step in robotic harvesting. Many researchers have investigated a variety of image analysis methods based on different imaging technologies for fruit recognition. However, challenges still occur in the implementation of this goal due to various factors, especially variable light and proximal color background. In this study, images with fruit were acquired with a Forward Looking Infrared (FLIR) camera based on the Multi-Spectral Dynamic Imaging (MSX) technology. In view of its imaging mechanism, the optimal timing and shooting angle for image acquisition were pre-analyzed to obtain the maximum contrast between fruit and background. An effective algorithm was developed for locking potential fruit regions, which was based on the pseudo-color and texture information from MSX images. The algorithm was applied to 506 training and 340 evaluating images, including a variety of fruit and complex backgrounds. Recognition precision and sensitivity of these complete fruit regions were both above 92%, and those of incomplete fruit regions were not lower than 72%. The average processing time for each image was less than 1 s. The results indicated that the developed algorithm based on MSX imaging was effective for fruit recognition and could be suggested as a potential method for the automation of orchard production.

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Lü, Q., M. j. Tang, J. r. Cai, J. w. Zhao, and S. Vittayapadung. "Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits." Czech Journal of Food Sciences 29, No. 6 (November 28, 2011): 595–602. http://dx.doi.org/10.17221/69/2010-cjfs.

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It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.We proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIR (408–1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using RBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

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Clark, Christopher J., Annette C. Richardson, and Ken B. Marsh. "Quantitative Magnetic Resonance Imaging of Satsuma Mandarin Fruit during Growth." HortScience 34, no. 6 (October 1999): 1071–75. http://dx.doi.org/10.21273/hortsci.34.6.1071.

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Whole-fruit proton magnetic resonance (MR) imaging was performed on satsuma mandarin (Citrus unshiu Markovich cv. Miho Wase) during a 15-week period commencing 10 weeks after anthesis and continuing to maturity, and at 6 weeks after anthesis the following season. Images with long repetition times (>1600 ms) and short echo times (20 ms) provided the clearest details of anatomical changes in the peel (flavedo, albedo) and vascular system, while those with similar repetition times but longer echo times (120 ms) were best for viewing juice sac morphology within pulp segments. At 6 weeks after anthesis, images of fruits of slightly different physiological ages highlighted rapid changes in the vascular bundles and albedo tissue at this stage of development. Variation in the relaxation measurements, T1 and T2, was determined from quantitative MR images of the juice sacs in equatorial slices, and images of expressed juice from whole fruit. Seasonal measurements of T1 determined in situ (1760 ms) were significantly greater than those in juice (1413 ms). By contrast, there was no mean seasonal difference between in situ T2 measurements (360 ms) and those for juice (332 ms). No associations between trends in the MR data and total soluble solids, pH, titratable acidity, and sugar and organic acid composition of the juice were established. Cell structure is identified as a hindrance in the use of quantitative MR imaging for probing compositional changes in solution in serial imaging studies.

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Malik, Aman Ullah. "Biotic and Abiotic Factors Causing Rind Blemishes in Citrus and Management Strategies to Improve the Cosmetic Quality of Fruits." International Journal of Agriculture and Biology 25, no. 02 (February 1, 2021): 298–318. http://dx.doi.org/10.17957/ijab/15.1670.

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Citrus is the major tree fruit crop grown and traded worldwide. Citrus industry around the globe has been facing deterioration in fruit cosmetic quality and increased farmgate rejections, mainly due to high incidence of rind blemishes. Rind blemishes are caused by various biotic (diseases and insects) and abiotic (environmental, physical and physiological) factors at various stages of fruit development, however initial 8–12 weeks of fruit setting are the most critical. While the causes and intensity of blemishes varies with agroecological conditions and citrus species, and fruits position in canopy, the blemishes due to melanose, scab, canker, thrips, mites, scales, and wind are generally the most common. Being a complex issue involving multiple factors in the field (environment, pathogens, tree, fruit, cultural practices), its control has always been very challenging. R&D progress overtime showed a great deal of work done on the subject, however for commercial success, an integrated approach is essential to reduce rind blemishes and improve fruits cosmetic quality. The key interventions include maintaining tree vigor and hygiene with judicial pruning, selective and timely application of pesticides at critical stages, particularly during initial 12 weeks of fruit development. Keeping in view the increasing concerns regarding food safety, the application of horticultural mineral oils (HMOs) and effective biological tools need to be integrated. To some degree, harvest and postharvest supply chain operations may also contribute towards some fruit blemishes (oleocellosis, rind/stem end breakdown, chilling injury, etc.) and are to be managed appropriately. While various advance technologies i.e., near-infrared (NIR), ultraviolet (UV), ultraviolet fluorescence (UVF), laser backscattering imaging (LBI) and hyperspectral imaging (HI) have been tested /developed for blemish-based fruit sorting, their high cost is prohibitive in adaptation particularly in developing countries. Future research needs to focus on assessing the impact of climate change on dynamics of biotic factors, blemish free fruit production under small tree-framework system, development of new chemistry low residue pesticides, reducing cost of high-tech sorting machines and consumer education to have acceptance of blemished fruit (still with good internal quality) to some degree. © 2021 Friends Science Publishers

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Gan, Hao, Won S. Lee, Victor Alchanatis, and A. Abd-Elrahman. "Active thermal imaging for immature citrus fruit detection." Biosystems Engineering 198 (October 2020): 291–303. http://dx.doi.org/10.1016/j.biosystemseng.2020.08.015.

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Yoshii, K., M. Fukuoka, K. Matsunaga, and T. Ikeda. "DIAGNOSTIC IMAGING OF WATERMELON FRUIT BY 1H-NMR." Acta Horticulturae, no. 936 (August 2012): 373–78. http://dx.doi.org/10.17660/actahortic.2012.936.49.

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Wang, Zhenglin, Anand Koirala, Kerry Walsh, Nicholas Anderson, and Brijesh Verma. "In Field Fruit Sizing Using A Smart Phone Application." Sensors 18, no. 10 (October 5, 2018): 3331. http://dx.doi.org/10.3390/s18103331.

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In field (on tree) fruit sizing has value in assessing crop health and for yield estimation. As the mobile phone is a sensor and communication rich device carried by almost all farm staff, an Android application (“FruitSize”) was developed for measurement of fruit size in field using the phone camera, with a typical assessment rate of 240 fruit per hour achieved. The application was based on imaging of fruit against a backboard with a scale using a mobile phone, with operational limits set on camera to object plane angle and camera to object distance. Image processing and object segmentation techniques available in the OpenCV library were used to segment the fruit from background in images to obtain fruit sizes. Phone camera parameters were accessed to allow calculation of fruit size, with camera to fruit perimeter distance obtained from fruit allometric relationships between fruit thickness and width. Phone geolocation data was also accessed, allowing for mapping fruits of data. Under controlled lighting, RMSEs of 3.4, 3.8, 2.4, and 2.0 mm were achieved in estimation of avocado, mandarin, navel orange, and apple fruit diameter, respectively. For mango fruit, RMSEs of 5.3 and 3.7 mm were achieved on length and width, benchmarked to manual caliper measurements, under controlled lighting, and RMSEs of 5.5 and 4.6 mm were obtained in-field under ambient lighting.

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Veranita, Dina, Minarni Minarni, Feri Candra, Saktioto Saktioto, and Mohammad Fisal Rabin. "Pencitraan Hiperspekral untuk Membedakan Asal Tanah Tumbuh Dari Tandan Buah Segar Kelapa Sawit." JURNAL MEDIA INFORMATIKA BUDIDARMA 4, no. 3 (July 20, 2020): 761. http://dx.doi.org/10.30865/mib.v4i3.2219.

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Hyperspectral imaging is a non destructive method that has been used to evaluate internal characteristics of fruits and vegetables. Plant genetics, soil characteristics, and plant management are some of key factors to define the quality of oil palm fresh fruit bunches (FFB) produced. This research was aimed to discriminate the Tenera oil palm FFBs produced by oil palm trees grown from mineral soil and peat soil using a hyperspectral imaging system which utilized a Specim V10 spektrograf. The discrimination was based on their ripeness level, mesocarp firmness, and classification using K-mean clustering. The samples consisted of 61 mineral soil FFBs and 60 peat soil FFBs with three ripeness levels as unripe, ripe, and overripe. Hyperspectral images were recorded and processed using Matlab programs. The spectral reflectance intensities showed the discrimination between both origin soils at wavelength ranges of 700 nm  900 nm. The results also showed higher reflectance intensities of peat soil FFBs than mineral soil FFBs. Correspondingly, Fruit firmness of peat soil FFBs are higher than mineral soil FFBs. Classification using K- mean clustering between reflectance intensities and fruit firmness showed significant clusters for three ripeness levels. These results will be useful for an oil palm FFB sorting machine based on spectral imaging method

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Ekramirad, Nader, Ahmed Rady, Akinbode A. Adedeji, and Reza Alimardani. "Application of Hyperspectral Imaging and Acoustic Emission Techniques for Apple Quality Prediction." Transactions of the ASABE 60, no. 4 (2017): 1391–401. http://dx.doi.org/10.13031/trans.12184.

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Abstract. There is a growing demand for developing effective non-destructive quality assessment methods with quick response, high accuracy, and low cost for fresh fruits. In this study, hyperspectral reflectance imaging (400 to 1000 nm) and acoustic emission (AE) tests were applied to ‘GoldRush’ apples (total number, n = 180) to predict fruit firmness, total soluble solids (TSS), and surface color parameters (L*, a*, b*) during an eight-week storage period. Partial least squares (PLS) regression, least squares support vector machine (LS-SVM), and multivariate linear regression (MLR) methods were used to establish models to predict the quality attributes of the apples. The results showed that hyperspectral imaging (HSI) could accurately predict all the attributes except TSS, while the AE method was capable of predicting fruit firmness, b* color index, and TSS. Overall, HSI regression using PLS had better comprehensive ability for predicting firmness, TSS, and color parameters (L*, a*, b*) than AE, with correlation coefficients of prediction (rp) of 0.92, 0.41, 0.83, 0.87, and 0.94 and root mean square errors of prediction (RMSEP) of 4.32 (N), 1.78 (°Brix), 3.41, 2.28, and 4.29, respectively, while AE regression using LS-SVM gave rp values of 0.88, 0.74, 0.34, 0.37, and 0.81 and RMSEP values of 4.26 (N), 0.64 (°Brix), 4.69, 1.8, and 5.17 for firmness, TSS, and color parameters (L*, a*, b*), respectively. The results show the potential of these two non-destructive methods for predicting some of the quality attributes of apples. Keywords: Apple, Acoustic emission, Fruit quality, Hyperspectral imaging, Regression model.

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Gao, Pan, Wei Xu, Tianying Yan, Chu Zhang, Xin Lv, and Yong He. "Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits." Foods 8, no. 12 (November 27, 2019): 620. http://dx.doi.org/10.3390/foods8120620.

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Narrow-leaved oleaster (Elaeagnus angustifolia) fruit is a kind of natural product used as food and traditional medicine. Narrow-leaved oleaster fruits from different geographical origins vary in chemical and physical properties and differ in their nutritional and commercial values. In this study, near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to identify the geographical origins of dry narrow-leaved oleaster fruits with machine learning methods. Average spectra of each single narrow-leaved oleaster fruit were extracted. Second derivative spectra were used to identify effective wavelengths. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to build discriminant models for geographical origin identification using full spectra and effective wavelengths. In addition, deep convolutional neural network (CNN) models were built using full spectra and effective wavelengths. Good classification performances were obtained by these three models using full spectra and effective wavelengths, with classification accuracy of the calibration, validation, and prediction set all over 90%. Models using effective wavelengths obtained close results to models using full spectra. The performances of the PLS-DA, SVM, and CNN models were close. The overall results illustrated that near-infrared hyperspectral imaging coupled with machine learning could be used to trace geographical origins of dry narrow-leaved oleaster fruits.

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BULANON, Duke M., Thomas F. BURKS, and Victor ALCHANATIS. "A Multispectral Imaging Analysis for Enhancing Citrus Fruit Detection." Environment Control in Biology 48, no. 2 (2010): 81–91. http://dx.doi.org/10.2525/ecb.48.81.

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Pathmanaban, P., B. K. Gnanavel, and Shanmuga Sundaram Anandan. "Recent application of imaging techniques for fruit quality assessment." Trends in Food Science & Technology 94 (December 2019): 32–42. http://dx.doi.org/10.1016/j.tifs.2019.10.004.

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Grover, Dhruv, Takeo Katsuki, and Ralph J. Greenspan. "Flyception: imaging brain activity in freely walking fruit flies." Nature Methods 13, no. 7 (May 16, 2016): 569–72. http://dx.doi.org/10.1038/nmeth.3866.

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Goodman, B. A., B. Williamson, E. J. Simpson, J. A. Chudek, G. Hunter, and D. A. M. Prior. "High field NMR microscopic imaging of cultivated strawberry fruit." Magnetic Resonance Imaging 14, no. 2 (January 1996): 187–96. http://dx.doi.org/10.1016/0730-725x(95)02051-t.

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Plasquy, Eddy, José M. Garcia, Maria C. Florido, and Rafael R. Sola-Guirado. "Estimation of the Cooling Rate of Six Olive Cultivars Using Thermal Imaging." Agriculture 11, no. 2 (February 17, 2021): 164. http://dx.doi.org/10.3390/agriculture11020164.

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Bringing the olive harvest period forward leads to storing fruit in field temperatures that risk jeopardizing its quality. Knowledge about the bio-thermal characteristics of olives is crucial when considering their cooling, although published research on the subject is limited. In this work, the cooling rate of the fruit of six olive cultivars has been empirically determined by measuring the evolution of their low temperature under controlled conditions by thermal imaging. Based on these data, the cooling time needed to cool the fruit to 22 °C was estimated, considering the biometric characteristics of the individual fruit, a field temperature from 26 to 42 °C, and a room cooling temperature from −8 to −20 °C. The results showed differences among the cultivars and the need to further investigate the specific heat requirements for small varieties and the impact of the conduction factor on the heavier ones. The simulation suggests that between 2 min (for the light Arbequina and Koroneiki cultivars) and 5 min (for the heavier Verdial and Gordal cultivars) suffice to cool the fruit to the desired temperature with a room temperature of −16 °C. These results show the feasibility of developing technological solutions for cooling olives before their industrial processing with industrial applications such as cooling tunnels on individual fruit.

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Bedini, Giacomo, Giorgia Bastianelli, Swathi Sirisha Nallan Chakravartula, Carmen Morales-Rodríguez, Luca Rossini, Stefano Speranza, Andrea Vannini, Roberto Moscetti, and Riccardo Massantini. "Feasibility of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging for sorting unsound chestnuts." Italus Hortus 27 (April 2020): 3–18. http://dx.doi.org/10.26353/j.itahort/2020.1.0318.

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Authors explored the potential use of Vis/NIR hyperspectral imaging (HSI) and Fourier-transform Near-Infrared (FT-NIR) spectroscopy to be used as in-line tools for the detection of unsound chestnut fruits (i.e. infected and/or infested) in comparison with the traditional sorting technique. For the intended purpose, a total of 720 raw fruits were collected from a local company. Chestnut fruits were preliminarily classified into sound (360 fruits) and unsound (360 fruits) batches using a proprietary floating system at the facility along with manual selection performed by expert workers. The two batches were stored at 4 ± 1 °C until use. Samples were left at ambient temperature for at least 12 h before measurements. Subsequently, fruits were subjected to non-destructive measurements (i.e. spectral analysis) immediately followed by destructive analyses (i.e. microbiological and entomological assays). Classification models were trained using the Partial Least Squares Discriminant Analysis (PLS-DA) by pairing the spectrum of each fruit with the categorical information obtained from its destructive assay (i.e., sound, Y = 0; unsound, Y = 1). Categorical data were also used to evaluate the classification performance of the traditional sorting method. The performance of each PLS-DA model was evaluated in terms of false positive error (FP), false negative error (FN) and total error (TE) rates. The best result (8% FP, 14% FN, 11% TE) was obtained using Savitzky-Golay first derivative with a 5-points window of smoothing on the dataset of raw reflectance spectra scanned from the hilum side of fruit using the Vis/NIR HSI setup. This model showed similarity in terms of False Negative error rate with the best one computed using data from the FT-NIR setup (i.e. 15% FN), which, however, had the lowest global performance (17% TE) due to the highest False Positive error rate (19%). Finally, considering that the total error rate committed by the traditional sorting system was about 14.5% with a tendency of misclassifying unsound fruits, the results indicate the feasibility of a rapid, in-line detection system based on spectroscopic measurements.

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Enomoto, Hirofumi, Masahiro Kotani, and Takayuki Ohmura. "Novel Blotting Method for Mass Spectrometry Imaging of Metabolites in Strawberry Fruit by Desorption/Ionization Using Through Hole Alumina Membrane." Foods 9, no. 4 (April 1, 2020): 408. http://dx.doi.org/10.3390/foods9040408.

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Mass spectrometry imaging (MSI) using matrix-assisted laser desorption/ionization (MALDI) is a powerful technique for visualizing metabolites in the strawberry fruit. During sample preparation for MALDI-MSI, sectioning of the samples is usually required. In general, MALDI-MSI analysis of strawberry fruits that are larger than a single glass slide is difficult because thin sections cannot be prepared. In this study, we attempted to visualize metabolites in large strawberry fruits by MSI, employing a blotting method that uses desorption ionization using a through-hole alumina membrane (DIUTHAME) chip. Large strawberry fruits were cut and a DIUTHAME chip was set on the cross-section to blot the metabolites. After drying the DIUTHAME chip, the metabolites were measured in positive and negative ion modes using a commercial MALDI-type mass spectrometer. Several peaks were detected in both the ion modes. Various metabolites related to food quality, such as sugars, organic acids, and anthocyanins, were detected and successfully visualized by blotting on a DIUTHAME chip in MSI. These results suggest that blotting using a DIUTHAME chip in MSI is useful for visualizing the metabolites present in the strawberry fruit.

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Li, Jiang Bo, Xiu Qin Rao, and Yi Bin Ying. "Inspection and Grading of Surface Defects of Fruits by Computer Vision." Advanced Materials Research 317-319 (August 2011): 956–61. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.956.

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Computer vision is a rapid, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy provide one alternative for an automated, non-destructive and cost-effective technique to accomplish ever-increasing production and quality requirements. This method of inspection has found applications in the agricultural industry, including the inspection and grading of fruits. This paper provides an introduction to main defection and grading approaches of fruit external defects, including image processing and pattern recognition methods based on fruit two-dimensional (2D) and three-dimensional (3D) information, and hyperspectral and multispectral imaging. In addition, their advantages and disadvantages are also discussed.

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Saranwong, Sirinnapa, Ronald P. Haff, Warunee Thanapase, Athit Janhiran, Sumaporn Kasemsumran, and Sumio Kawano. "A Feasibility Study Using Simplified near Infrared Imaging to Detect Fruit Fly Larvae in Intact Fruit." Journal of Near Infrared Spectroscopy 19, no. 1 (January 2011): 55–60. http://dx.doi.org/10.1255/jnirs.915.

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Taylor, Jennifer C., Carolyn Sutter, Lenna L. Ontai, Adrienne Nishina, and Sheri Zidenberg-Cherr. "Comparisons of school and home-packed lunches for fruit and vegetable dietary behaviours among school-aged youths." Public Health Nutrition 22, no. 10 (February 26, 2019): 1850–57. http://dx.doi.org/10.1017/s136898001900017x.

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AbstractObjectiveSchool-based interventions and policies encourage youths to include and consume fruits and vegetables at lunchtime via school lunches, but limited research has examined how these behaviours compare when youths have home-packed lunches. The objective of the present study was to compare fruit and vegetable contents and consumption among students having school or home-packed lunches over the school week.DesignParticipants were observed over five consecutive days at school lunchtime. Trained analysts estimated students’ lunchtime fruit and vegetable contents and consumption using digital imaging. Mixed models examined associations between fruit and vegetable dietary behaviours and lunch source (school v. home-packed), controlling for student gender, grade and school.SettingThree elementary schools in northern California, USA.ParticipantsFourth-, fifth- and sixth-grade students (nchildren 315; nobservations 1421).ResultsStudents were significantly less likely to have and to consume fruits and vegetables (all P<0·05) when having home-packed lunches, compared with when having school lunches. Among those who did have or did consume these foods, having a home-packed lunch was associated with consuming significantly less fruit (P<0·05) but no differences for other dietary outcomes.ConclusionsThe study adds to a growing body of literature indicating shortfalls in fruit and vegetable contents and consumption associated with having a home-packed lunch, relative to having a school lunch. Findings suggest that school-based interventions, particularly when targeting home-packed lunches, should focus on whether or not these foods are included and consumed, with less emphasis on quantities.

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Munera, Sandra, Alejandro Rodríguez-Ortega, Nuria Aleixos, Sergio Cubero, Juan Gómez-Sanchis, and José Blasco. "Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics." Foods 10, no. 9 (September 13, 2021): 2170. http://dx.doi.org/10.3390/foods10092170.

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The main cause of flesh browning in ‘Rojo Brillante’ persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450–1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through an algorithm based on principal component analysis (PCA). The aim was to automatically separate intact and damaged fruit, and to detect the damage in the PC images when present. With this algorithm, 90.0% of intact fruit and 90.8% of damaged fruit were correctly detected. A model based on partial least squares—discriminant analysis (PLS-DA), was later calibrated using the mean spectrum of the pixels detected as damaged, to determine the moment when the fruit was damaged. The model differentiated fruit corresponding correctly to 0, 1, 2 and 3 days after damage induction, achieving a total accuracy of 99.4%.

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Kämper, Wiebke, Stephen J. Trueman, Iman Tahmasbian, and Shahla Hosseini Bai. "Rapid Determination of Nutrient Concentrations in Hass Avocado Fruit by Vis/NIR Hyperspectral Imaging of Flesh or Skin." Remote Sensing 12, no. 20 (October 17, 2020): 3409. http://dx.doi.org/10.3390/rs12203409.

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Fatty acid composition and mineral nutrient concentrations can affect the nutritional and postharvest properties of fruit and so assessing the chemistry of fresh produce is important for guaranteeing consistent quality throughout the value chain. Current laboratory methods for assessing fruit quality are time-consuming and often destructive. Non-destructive technologies are emerging that predict fruit quality and can minimise postharvest losses, but it may be difficult to develop such technologies for fruit with thick skin. This study aimed to develop laboratory-based hyperspectral imaging methods (400–1000 nm) for predicting proportions of six fatty acids, ratios of saturated and unsaturated fatty acids, and the concentrations of 14 mineral nutrients in Hass avocado fruit from 219 flesh and 194 skin images. Partial least squares regression (PLSR) models predicted the ratio of unsaturated to saturated fatty acids in avocado fruit from both flesh images (R2 = 0.79, ratio of prediction to deviation (RPD) = 2.06) and skin images (R2 = 0.62, RPD = 1.48). The best-fit models predicted parameters that affect postharvest processing such as the ratio of oleic:linoleic acid from flesh images (R2 = 0.67, RPD = 1.63) and the concentrations of boron (B) and calcium (Ca) from flesh images (B: R2 = 0.61, RPD = 1.51; Ca: R2 = 0.53, RPD = 1.71) and skin images (B: R2 = 0.60, RPD = 1.55; Ca: R2 = 0.68, RPD = 1.57). Many quality parameters predicted from flesh images could also be predicted from skin images. Hyperspectral imaging represents a promising tool to reduce postharvest losses of avocado fruit by determining internal fruit quality of individual fruit quickly from flesh or skin images.

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Wei, Xuan, Jin-Cheng He, Da-Peng Ye, and Deng-Fei Jie. "Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging." Journal of Food Quality 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/1023498.

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Maturity grading is important for the quality of fruits. Nondestructive maturity detection can be greatly beneficial to the consumer and fruit industry. In this paper, a hyperspectral image of navel oranges was obtained using a diffuse transmittance imaging based system. Multispectral indexes were built to identify the maturity with the hyperspectral technique. Five indexes were proposed to combine the spectra at wavelengths of 640, 760 nm (red edges), and 670 nm (for chlorophyll content) to grade the navel oranges into three maturity stages. The index of (T670+T760-T640)/(T670+T760+T640) seemed to be more appropriate to classify maturity, especially to distinguish immature oranges that can be straightly identified in accordance with the value of this index ((T670+T760-T640)/(T670+T760+T640)). Different indexes were used as the input of linear discriminate analysis (LDA) and of k-nearest neighbor (k-NN) algorithm to identify the maturity, and it was found that k-NN with (T670+T760-T640)/(T670+T760+T640) could reach the highest correct classification rate of 96.0%. The results showed that the built index was feasible and accurate in the nondestructive classification of oranges based on the hyperspectral diffuse transmittance imaging. It will greatly help to develop low-cost and real-time multispectral imaging systems for the nondestructive detection of fruit quality in the industry.

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Ripoll, Juan-José, Mingyuan Zhu, Stephanie Brocke, Cindy T. Hon, Martin F. Yanofsky, Arezki Boudaoud, and Adrienne H. K. Roeder. "Growth dynamics of the Arabidopsis fruit is mediated by cell expansion." Proceedings of the National Academy of Sciences 116, no. 50 (November 22, 2019): 25333–42. http://dx.doi.org/10.1073/pnas.1914096116.

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Fruit have evolved a sophisticated tissue and cellular architecture to secure plant reproductive success. Postfertilization growth is perhaps the most dramatic event during fruit morphogenesis. Several studies have proposed that fertilized ovules and developing seeds initiate signaling cascades to coordinate and promote the growth of the accompanying fruit tissues. This dynamic process allows the fruit to conspicuously increase its size and acquire its final shape and means for seed dispersal. All these features are key for plant survival and crop yield. Despite its importance, we lack a high-resolution spatiotemporal map of how postfertilization fruit growth proceeds at the cellular level. In this study, we have combined live imaging, mutant backgrounds in which fertilization can be controlled, and computational modeling to monitor and predict postfertilization fruit growth in Arabidopsis. We have uncovered that, unlike leaves, sepals, or roots, fruit do not exhibit a spatial separation of cell division and expansion domains; instead, there is a separation into temporal stages with fertilization as the trigger for transitioning to cell expansion, which drives postfertilization fruit growth. We quantified the coordination between fertilization and fruit growth by imaging no transmitting tract (ntt) mutants, in which fertilization fails in the bottom half of the fruit. By combining our experimental data with computational modeling, we delineated the mobility properties of the seed-derived signaling cascades promoting growth in the fruit. Our study provides the basis for generating a comprehensive understanding of the molecular and cellular mechanisms governing fruit growth and shape.

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Noh, Hyun Kwon, and Renfu Lu. "Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality." Postharvest Biology and Technology 43, no. 2 (February 2007): 193–201. http://dx.doi.org/10.1016/j.postharvbio.2006.09.006.

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González-Cabrera, M., A. Domínguez-Vidal, and M. J. Ayora-Cañada. "Hyperspectral FTIR imaging of olive fruit for understanding ripening processes." Postharvest Biology and Technology 145 (November 2018): 74–82. http://dx.doi.org/10.1016/j.postharvbio.2018.06.008.

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Kuban, Deborah, Anas El-mahdi, and Paul Schellhammer. "Fruit for thought." International Journal of Radiation Oncology*Biology*Physics 15, no. 3 (September 1988): 796–97. http://dx.doi.org/10.1016/0360-3016(88)90334-3.

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Saltveit, M. E. "CORRELATION AMONG CHANGES IN THE NUCLEAR MAGNETIC RESONANCE IMAGE OF RIPENING TOMATO FRUIT AND OTHER RIPENING PARAMETERS." HortScience 25, no. 9 (September 1990): 1132d—1132. http://dx.doi.org/10.21273/hortsci.25.9.1132d.

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Tomato fruit undergo an orderly series of physiological and morphological changes as they progress from mature-green (MG) to red-ripe. Fruit are commercially harvested at the MG stage, a stage which often encompasses fruit of varying degrees of maturity. The ability to predict the time required for MG fruit to ripen would reduce variability in experiments and could be commercially used to pack fruit that would ripen uniformly. Nuclear magnetic resonance (NMR) imaging can nondestructively measure internal changes associated with plant growth and developmental. In this study, NMR images were taken of freshly harvested tomato fruit (Lycopersicum esculentum cv. Castlemart) at different stages of maturity and ripeness. Measurements were also made of the stage of ripeness, rate of respiration and ethylene production, lycopene and chlorophyll content, density of the pericarp wall, and condition of locular tissue. NMR images showed substantial charges in the pericarp wall and locular tissue during maturation and ripening of tomato fruit. However, it was difficult to objectively evaluate these visual changes with other ripening parameters. For example, increased lightness and graininess of the pericarp wall image was associated with a decrease in wall density; while lightening of the locular image was associated with tissue liquefacation. Use of NMR imaging in studies of tomato fruit ripening will be discussed.

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Zhang, Mengyun, Changying Li, Fumiomi Takeda, and Fuzeng Yang. "Detection of Internally Bruised Blueberries Using Hyperspectral Transmittance Imaging." Transactions of the ASABE 60, no. 5 (2017): 1489–502. http://dx.doi.org/10.13031/trans.12197.

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Abstract. Internal bruise damage that occurs in blueberry fruit during harvest operations and postharvest handling lowers the overall quality and causes significant economic losses. The main goal of this study was to nondestructively detect internal bruises in blueberries after mechanical damage using hyperspectral transmittance imaging. A total of 600 hand-harvested blueberries were divided into 20 groups of four storage times (30 min, 3 h, 12 h, and 24 h), two storage temperatures (22°C and 4°C), and three treatments (stem bruise, equator bruise, and control). A near-infrared hyperspectral imaging system was used to acquire transmittance images from 970 to 1400 nm with 5 nm bandwidth. Images were acquired from three orientations (calyx-up, stem-up, and equator-up) for fruit in the control and stem bruise groups and from four orientations (calyx-up, stem-up, equator-up, and equator-down) in the equator bruise groups. Immediately after imaging, the fruit samples were sliced, and the sliced surfaces were photographed. The color images of sliced fruit were used as references. By comparing with the reference color images, the profiles of spatial and spectral intensities were evaluated to observe the effect of orientation and help extract regions of interest (ROIs) of bruised and healthy tissues. A support vector machine (SVM) classifier was trained and tested to classify pixels of bruised and healthy tissues. Classification maps were produced, and the bruise ratio was calculated to identify bruised blueberries (bruise ratio >25%). The average accuracy of blueberry identification was 94.5% with the stem-up orientation. The results indicate that detecting bruised blueberries as soon as 30 min after mechanical damage is feasible using hyperspectral transmittance imaging. Keywords: Blueberry, Bruise detection, Classification, Hyperspectral imagery, Transmittance mode.

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Lin, W. C., and P. A. Jolliffe. "Light Intensity and Spectral Quality Affect Fruit Growth and Shelf Life of Greenhouse-grown Long English Cucumber." Journal of the American Society for Horticultural Science 121, no. 6 (November 1996): 1168–73. http://dx.doi.org/10.21273/jashs.121.6.1168.

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The importance of light intensity and spectral quality on fruit color and shelf life of long English cucumber (Cucumis sativus L.) was studied in four greenhouse experiments. The intensity of cucumber greenness was measured nondestructively by video imaging, and shelf life was measured by visual observation of incipient yellowing. In the summer, filters were used to cover individual fruit to reduce light intensity reaching the fruit surface. The lower the light intensity incident on a cucumber, the shorter its shelf life. The average shelf life was 8, 5, or 1 days for cucumbers receiving 100%, 66%, or 31% of natural daylight, respectively. The fruit that were covered with a filter transmitting red (R) light were greener (low grey level via video imaging) than those with a far-red (FR) filter. In the fall, fruit receiving spectral R lighting from fluorescence tubes were greener and had a longer shelf life than those receiving FR lighting from incandescent bulbs. In the winter, high-pressure sodium (HPS) lighting was necessary to supplement natural daylight for crop growth and production. Under HPS, R and FR lighting produced the same fruit greenness and shelf life. In the spring, R-lighted fruit had longer shelf life than FR-lighted ones, although fruit color at harvest was similar. In these four experiments, postharvest shelf life of long English cucumber was generally related to fruit greenness upon harvest. The data suggest the importance of an open canopy in improving fruit greenness and shelf life of greenhouse-grown cucumbers.

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Enomoto, Hirofumi. "Mass Spectrometry Imaging of Flavonols and Ellagic Acid Glycosides in Ripe Strawberry Fruit." Molecules 25, no. 20 (October 9, 2020): 4600. http://dx.doi.org/10.3390/molecules25204600.

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Flavonols and ellagic acid glycosides are major phenolic compounds in strawberry fruit. They have antioxidant activity, show protective functions against abiotic and biotic stress, and provide health benefits. However, their spatial distribution in ripe fruit has not been understood. Therefore, matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) was performed to investigate their distribution in fruit tissues. Using strawberry extract, five flavonols, namely, three kaempferols and two quercetins, and two ellagic acid glycosides, were tentatively identified by MALDI-tandem MS. To investigate the tentatively identified compounds, MALDI-MSI and tandem MS imaging (MS/MSI) analyses were performed. Kaempferol and quercetin glycosides showed similar distribution patterns. They were mainly found in the epidermis, while ellagic acid glycosides were mainly found in the achene and in the bottom area of the receptacle. These results suggested that the difference in distribution pattern between flavonols and ellagic acid glycosides depends on the difference between their aglycones. Seemingly, flavonols play a role in protective functions in the epidermis, while ellagic acid glycosides play a role in the achene and in the bottom side of the receptacle, respectively. These results demonstrated that MALDI-MSI is useful for distribution analysis of flavonols and ellagic acid glycosides in strawberry fruit.

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Giefer, Lino Antoni, Juan Daniel Arango Castellanos, Mohammad Mohammadzadeh Babr, and Michael Freitag. "Deep Learning-Based Pose Estimation of Apples for Inspection in Logistic Centers Using Single-Perspective Imaging." Processes 7, no. 7 (July 4, 2019): 424. http://dx.doi.org/10.3390/pr7070424.

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Fruit packaging is a time-consuming task due to its low automation level. The gentle handling required by some kinds of fruits and their natural variations complicates the implementation of automated quality controls and tray positioning for final packaging. In this article, we propose a method for the automatic localization and pose estimation of apples captured by a Red-Green-Blue (RGB) camera using convolutional neural networks. Our pose estimation algorithm uses a cascaded structure composed of two independent convolutional neural networks: one for the localization of apples within the images and a second for the estimation of the three-dimensional rotation of the localized and cropped image area containing an apple. We used a single shot multi-box detector to find the bounding boxes of the apples in the images. Lie algebra is used for the regression of the rotation, which represents an innovation in this kind of application. We compare the performances of four different network architectures and show that this kind of representation is more suitable than using state-of-the-art quaternions. By using this method, we achieved a promising accuracy for the rotation regression of 98.36%, considering an error range lower than 15 degrees, forming a base for the automation of fruit packing systems.

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Lee, Ah-yeong, Sang-Yeon Kim, Suk-Ju Hong, Yun-hyeok Han, Younghun Choi, Minyoung Kim, Seok Kyu Yun, and Ghiseok Kim. "Phenotypic Analysis of Fruit Crops Water Stress Using Infrared Thermal Imaging." Journal of Biosystems Engineering 44, no. 2 (June 1, 2019): 87–94. http://dx.doi.org/10.1007/s42853-019-00020-2.

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Lorente, D., M. Zude, C. Regen, L. Palou, J. Gómez-Sanchis, and J. Blasco. "Early decay detection in citrus fruit using laser-light backscattering imaging." Postharvest Biology and Technology 86 (December 2013): 424–30. http://dx.doi.org/10.1016/j.postharvbio.2013.07.021.

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Joyce, Daryl C., Paul D. Hockings, Roy A. Mazucco, and Anthony J. Shorter. "1H-Nuclear magnetic resonance imaging of ripening 'Kensington Pride' mango fruit." Functional Plant Biology 29, no. 7 (2002): 873. http://dx.doi.org/10.1071/pp01150.

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Physicochemical gradients occur in mango mesocarp tissue during ripening. These gradients are reflected in water activity, which is non-uniform throughout the mesocarp. Signal intensity in proton magnetic resonance images (first echo, proton density and T2) for green-mature `Kensington Pride' mesocarp tissue was highest near the endocarp and lowest near the exocarp. Relative signal intensity increased in the middle mesocarp as ripening proceeded, but remained relatively low in the outer mesocarp. T2 relaxation times for inner and middle mesocarp regions fell during ripening. The data suggest that water activity in the mesocarp tissue increased in an outward-moving flux as ripening progressed. This change in water activity was associated with starch hydrolysis and other ripening-related processes that commence near the endocarp.

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Rajkumar, P., N. Wang, G. EImasry, G. S. V. Raghavan, and Y. Gariepy. "Studies on banana fruit quality and maturity stages using hyperspectral imaging." Journal of Food Engineering 108, no. 1 (January 2012): 194–200. http://dx.doi.org/10.1016/j.jfoodeng.2011.05.002.

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Defraeye, Thijs, Bart Nicolaï, David Mannes, Wondwosen Aregawi, Pieter Verboven, and Dominique Derome. "Probing inside fruit slices during convective drying by quantitative neutron imaging." Journal of Food Engineering 178 (June 2016): 198–202. http://dx.doi.org/10.1016/j.jfoodeng.2016.01.023.

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Journal articles on the topic 'Fruit imaging'

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