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Relevant bibliographies by topics / Fruit sizing

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Academic literature on the topic 'Fruit sizing'

Author: Grafiati

Published: 17 March 2025

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Journal articles on the topic "Fruit sizing"

1

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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Neupane, Chiranjivi, Maisa Pereira, Anand Koirala, and Kerry B. Walsh. "Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning." Sensors 23, no. 8 (April 10, 2023): 3868. http://dx.doi.org/10.3390/s23083868.

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Forward estimates of harvest load require information on fruit size as well as number. The task of sizing fruit and vegetables has been automated in the packhouse, progressing from mechanical methods to machine vision over the last three decades. This shift is now occurring for size assessment of fruit on trees, i.e., in the orchard. This review focuses on: (i) allometric relationships between fruit weight and lineal dimensions; (ii) measurement of fruit lineal dimensions with traditional tools; (iii) measurement of fruit lineal dimensions with machine vision, with attention to the issues of depth measurement and recognition of occluded fruit; (iv) sampling strategies; and (v) forward prediction of fruit size (at harvest). Commercially available capability for in-orchard fruit sizing is summarized, and further developments of in-orchard fruit sizing by machine vision are anticipated.

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3

Buchner, Richard, Seeley Mudd, Bruce Carroll, and Mark Gilles. "Harvest Field Sizing as a Technique to Remove Undersize French Prunes." HortScience 33, no. 3 (June 1998): 452a—452. http://dx.doi.org/10.21273/hortsci.33.3.452a.

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Overall profitability is a major goal in successful prune production and a major component in any prune management system. Large prune crops in 1996 and 1997 have stimulated considerable interest in undersize fruit. Undersize prunes currently have marginal value and may represent a net loss because of costs to haul, dry, and to market order payments on low value prunes. One technique to control delivery size is to field size at harvest. Field sizing involves installing size-sorting devices on harvesters, which allow small prunes to fall out while valuable fruit is collected. Field sizing is considered a “risky” strategy because of the potential to remove prunes with economic value. During the 1997 harvest, 21 infield harvest sizing evaluations were made in prune orchards throughout Tehama county. The first evaluation occurred on 12 Aug. 1997, at the start of prune harvest. The final evaluation was done on 5 Sept. 1997, at the tail end of harvest. The objective was to sample throughout the harvest period to test field sizing under various sugar, size, and fruit pressure scenarios. The test machine was 1-inch bar sizer. Of the 21 sample dates, undersize fruit was clearly not marketable in 20 of the 21 samples. Discarded fruit averaged 133 dry count per pound. Only one sample out of 21 may have had market value at 86 dry count per pound. Although small in size, these prunes had very high sugar content contributing to their dry weight. In this evaluation, a 1-inch bar sizer did a good job of separating fruit with and without market value under the 1997 price schedule. As harvest date becomes later and soluble solids increase, the chances of sorting out marketable prunes also increases.

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Koirala, A., Z. Wang, K. B. Walsh, and C. McCarthy. "Fruit sizing in-field using a mobile app." Acta Horticulturae, no. 1244 (July 2019): 129–36. http://dx.doi.org/10.17660/actahortic.2019.1244.20.

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5

Amaral, Marcelo H., and Kerry B. Walsh. "In-Orchard Sizing of Mango Fruit: 2. Forward Estimation of Size at Harvest." Horticulturae 9, no. 1 (January 3, 2023): 54. http://dx.doi.org/10.3390/horticulturae9010054.

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Forecast of tree fruit yield requires prediction of harvest time fruit size as well as fruit number. Mango (Mangifera indica L.) fruit mass can be estimated from correlation to measurements of fruit length (L), width (W) and thickness (T). On-tree measurements of individually tagged fruit were undertaken using callipers at weekly intervals until the fruit were past commercial maturity, as judged using growing degree days (GDD), for mango cultivars ‘Honey Gold’, ‘Calypso’ and ‘Keitt’ at four locations in Australia and Brazil during the 2020/21 and 21/22 production seasons. Across all cultivars, the linear correlation of fruit mass to LWT was characterized by a R2 of 0.99, RMSE of 29.9 g and slope of 0.5472 g/cm3, while the linear correlation of fruit mass to )2, mimicking what can be measured by machine vision of fruit on tree, was characterized by a R2 of 0.97, RMSE of 25.0 g and slope of 0.5439 g/cm3. A procedure was established for the prediction of fruit size at harvest based on measurements made five and four or four and three weeks prior to harvest (approx. 514 and 422 GDD, before harvest, respectively). Linear regression models on weekly increase in fruit mass estimated from lineal measurements were characterized by an R2 > 0.88 for all populations, with an average slope (rate of increase) of 19.6 ± 7.1 g/week, depending on cultivar, season and site. The mean absolute percentage error for predicted mass compared to harvested fruit weight for estimates based on measurements of the earlier and later intervals was 16.3 ± 1.3% and 4.5 ± 2.4%, respectively. Measurement at the later interval allowed better accuracy on prediction of fruit tray size distribution. A recommendation was made for forecast of fruit mass at harvest based on in-field measurements at approximately 400 to 450 GDD units before harvest GDD and one week later.

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Peterson, D. L. "Harvest Mechanization Progress and Prospects for Fresh Market Quality Deciduous Tree Fruits." HortTechnology 15, no. 1 (January 2005): 72–75. http://dx.doi.org/10.21273/horttech.15.1.0072.

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Deciduous tree fruit crops such as apple (Malus domestica), peach (Prunus persica), and sweet cherry (Prunus avium) are not mechanically harvested for the fresh market. Attempts to mechanically harvest these fruits by mass removal techniques have not been successful due to excessive fruit damage caused during detachment, fall through the canopy, and collection. Robotic harvesters have not been commercially accepted due to insufficient fruit recovery. A U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) harvesting concept shows promise for harvesting both fresh market quality apples and sweet cherries. Successful mechanical harvesting of fresh market quality deciduous tree fruit will only occur when plant characteristics and machine designs are integrated into a compatible system. Cultivar characteristics that would facilitate machine harvesting are uniform fruit maturity at harvest, firm fruit that are resistant to mechanical damage, and compact growth habit that produces fruit in narrow canopies and on short/stiff limbs. Engineers must develop new detachment principles that minimize the energy input to effect fruit detachment, and develop durable energy-absorbing catching surfaces/conveyors to eliminate damage during collection of the fruit. As technology advances, sorting and sizing systems might be developed that can be operating on the harvester to eliminate culls in the field and deliver only fresh market quality fruit to the packers.

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7

Russo, J. M., and E. J. Holcomb. "Gauges for Discrete Plant Measurement." HortScience 21, no. 1 (February 1986): 149. http://dx.doi.org/10.21273/hortsci.21.1.149.

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Abstract Simple, specialized instruments, such as tree calipers, tree diameter tapes, and fruit sizing templates, have been used to quantify dimensions of specific plant parts and provide a standard set of plant measurements. Forshey (3) used a band-type fruit caliper to measure the diameter of McIntosh apples to predict their size at time of harvest.

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Jarimopas, B., S. Toomsaengtong, and C. Inprasit. "Design and testing of a mangosteen fruit sizing machine." Journal of Food Engineering 79, no. 3 (April 2007): 745–51. http://dx.doi.org/10.1016/j.jfoodeng.2006.01.083.

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9

Hong, Suk-Ju, Sungjay Kim, ChangHyup Lee, Seongmin Park, Kyoung-Chul Kim, Ahyeong Lee, and Ghiseok Kim. "On-Plant Size and Weight Estimation of Tomato Fruits Using Deep Neural Networks and RGB-D Imaging." Journal of the ASABE 67, no. 2 (2024): 439–50. http://dx.doi.org/10.13031/ja.15746.

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Highlights Deep learning-based instance segmentation models were applied and evaluated for tomato fruit detection. Mask R-CNN with vision transformer backbone showed the highest accuracy for tomato instance detection. Size and weight estimation indexes were calculated using tomato region depth data from instance segmentation models. Area-based index has higher accuracy for weight estimation than indexes based on weight and height information. Abstract. The size and weight of fruits are crucial factors in yield prediction and determining harvesting time. Machine vision, including fruit detection, is a key technology in the automated monitoring and harvesting of fruits. In particular, deep learning-based fruit-detection methods have been actively applied. Estimation of fruit size after fruit detection requires depth information, which can be acquired using depth imaging. RGB-D cameras include color and depth information required for fruit detection and size estimation. In this study, the RGB-D imaging technique was used to estimate the size and weight of tomatoes. Furthermore, deep learning-based instance segmentation models, including Mask R-CNN, YOLACT, and RTMDet for tomato fruit detection, were trained and evaluated. The proposed method estimated the fruit width with a root mean square error (RMSE) of 4 mm, a mean absolute percentage error (MAPE) of 4.28%, and a fruit height with an RMSE of 5.12 mm and a MAPE of 6.42%. Furthermore, the weight-prediction model based on the area index estimated the tomato fruit weight with an RMSE of 19.69 g and MAPE of 9.44%. Thus, the method can be used for accurate size and weight estimation and can be applied in growth monitoring and automated tomatoes harvesting. Keywords: Deep learning, Fruit sizing, Instance segmentation, RGB-D, Tomato.

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Miranda, Juan C., Jordi Gené-Mola, Manuela Zude-Sasse, Nikos Tsoulias, Alexandre Escolà, Jaume Arnó, Joan R. Rosell-Polo, Ricardo Sanz-Cortiella, José A. Martínez-Casasnovas, and Eduard Gregorio. "Fruit sizing using AI: A review of methods and challenges." Postharvest Biology and Technology 206 (December 2023): 112587. http://dx.doi.org/10.1016/j.postharvbio.2023.112587.

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Dissertations / Theses on the topic "Fruit sizing"

1

Butler, Marvin, and Bob Rush. "Gibberellic Acid Sizing Trial on Table Grapes, 1987." College of Agriculture, University of Arizona (Tucson, AZ), 1990. http://hdl.handle.net/10150/215713.

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Butler, Marvin, and Bob Rush. "Gibberellic Acid Sizing Trial on Table Grapes, 1988." College of Agriculture, University of Arizona (Tucson, AZ), 1990. http://hdl.handle.net/10150/215729.

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Butler, Marvin, and Bob Rush. "Gibberellic Acid Sizing Trial on Table Grapes, 1989." College of Agriculture, University of Arizona (Tucson, AZ), 1990. http://hdl.handle.net/10150/215730.

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4

Brulard, Nicolas. "Outils d'aide à la conception de systèmes de production maraîchers urbains optimisés pour la vente en circuits courts et de proximité." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAI002.

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Pour accompagner le développement des fermes urbaines professionnelles et des circuits courts et de proximité dans les grandes métropoles, nous proposons des outils à base de programmes mathématiques d'aide à la conception de fermes maraîchères diversifiées ciblant les demandes de différents types de clients en fruits et légumes frais locaux. Les solutions techniques de culture en ville se développent rapidement, mais compte-tenu des contraintes fortes de la production agricole urbaine (foncier limité, coûts opérationnels élevés), la définition de modèles économiques viables pour des fermes pérennes est un vrai défi pour les maraîchers urbains. Trois modèles en programmation linéaire mixte sont présentés et confrontés aux résultats du terrain : un modèle de dimensionnement stratégique annuel, un modèle de sélection de la meilleure combinaison de clients et un modèle de dimensionnement stratégique pluri-annuel des fermes maraîchères. Des résultats numériques et les performances des modèles sont présentés à partir de cas concrets multi-produits, multi-techniques et multi-périodes. Nos contributions résident dans la prise en compte de la périssabilité des produits frais dans le dimensionnement stratégique des systèmes de production, incluant le dimensionnement de la main d’œuvre agricole
To support the urban farm emergence trend in large metropolises, we propose decision support tools based on mathematical programs to design market gardening farms targetting the demands of different categories of clients in local fresh fruits and vegetables. Technical solutions develop rapdily, but the strong constraints linked to urban farming, such as limited surface and high operating costs, make difficult to define viable and sustainable business models for urban market gardeners. Three mixed integer linear programming models are presented: An annual strategic sizing model, a client combination selection model and a plurennial strategic sizing model for diversified fruit and vegetable farms. Numerical results and model performances are presented, based on multi-products, multi-techniques and multi-periods real cases. Our main contributions are the consideration of the perishable nature of fruits and vegetables in strategic production systems sizing models, including notably the investments and workforce sizing

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Books on the topic "Fruit sizing"

1

Ontario. Ministry of Agriculture and Food. Sizing and laying out a short-term (summer) refrigerated storage for fruits and vegetables. Toronto, Ont: Ministry of Agriculture and Food, 1992.

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2

McKnight, Tyris. Low Hanging Fruit: When Love Is Addicting and Skin-Deep but Problems Are Everywhere, You Need to Decide to Fight or Let It Go! Sizzling Romance Novel. Independently Published, 2021.

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Book chapters on the topic "Fruit sizing"

1

García, Enrique, and José Ivars. "Sizing, Peeling, Cutting, and Sorting of Fruits and Vegetables." In Operations in Food Refrigeration, 73–92. CRC Press, 2012. http://dx.doi.org/10.1201/b12137-7.

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Conference papers on the topic "Fruit sizing"

1

Solaiyappan, V. A., T. Yuvaraj, M. Thirumalai, N. Sivasankar, and K. R. Devabalaji. "Optimal sitting and sizing of DSTATCOM using fruit fly algorithm." In INTERNATIONAL CONFERENCE ON TRENDS IN CHEMICAL ENGINEERING 2021 (ICoTRiCE2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0114595.

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2

Huang, Haibo, Juan Zhang, and Dinghui Wu. "Optimal Sizing of Energy Storage Based on Improved Fruit Fly Algorithm." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164063.

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3

Bortolotti, G., M. Gullino, M. Piani, D. Mengoli, and L. Manfrini. "67. Apple fruit sizing through low-cost depth camera and neural network application." In 14th European Conference on Precision Agriculture. The Netherlands: Wageningen Academic Publishers, 2023. http://dx.doi.org/10.3920/978-90-8686-947-3_67.

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