Mass and Volume Estimation of Passion Fruit using Digital Images

Gonzalez, Juan Pablo Bonilla; Prieto, Flavio; Rodriguez, Claudia Patricia Perez

doi:10.1109/tla.2017.7854623

Cited by 18 publications

(4 citation statements)

References 13 publications

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“…The estimation of mass of a mango fruit on-tree from lineal dimensions is prone to greater error than for a circular-symmetric fruit, given the variation in mango fruit width in different orientations. Thus, while the RMSE on estimation of fruit lineal dimension in this study (4.7 mm) (Table 8) was comparable to that achieved in other studies and other fruit, e.g., 4.9 mm RMSE for mango length [9] and 5.1 mm RMSE for apple diameter at 40% visibility of fruit surface [6], the RMSE on estimation of mass was large, at 113 g, compared to that for axi-symmetric fruit, e.g., 18 g for tomato [23] and 15.5 g or for passion fruit [30]. Authors of [31] reported 95 and 96.7% accuracy on mass estimates for carrot and cucumber, respectively.…”

Section: Fruit Weight Estimationsupporting

confidence: 87%

In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation

2022

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Estimation of fruit size on-tree is useful for yield estimation, harvest timing and market planning. Automation of measurement of fruit size on-tree is possible using RGB-depth (RGB-D) cameras, if partly occluded fruit can be removed from consideration. An RGB-D Time of Flight camera was used in an imaging system that can be driven through an orchard. Three approaches were compared, being: (i) refined bounding box dimensions of a YOLO object detector; (ii) bounding box dimensions of an instance segmentation model (Mask R-CNN) applied to canopy images, and (iii) instance segmentation applied to extracted bounding boxes from a YOLO detection model. YOLO versions 3, 4 and 7 and their tiny variants were compared to an in-house variant, MangoYOLO, for this application, with YOLO v4-tiny adopted. Criteria developed to exclude occluded fruit by filtering based on depth, mask size, ellipse to mask area ratio and difference between refined bounding box height and ellipse major axis. The lowest root mean square error (RMSE) of 4.7 mm and 5.1 mm on the lineal length dimensions of a population (n = 104) of Honey Gold and Keitt varieties of mango fruit, respectively, and the lowest fruit exclusion rate was achieved using method (ii), while the RMSE on estimated fruit weight was 113 g on a population weight range between 180 and 1130 g. An example use is provided, with the method applied to video of an orchard row to produce a weight frequency distribution related to packing tray size.

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Section: Fruit Weight Estimationsupporting

confidence: 87%

In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation

2022

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“…At present, there are many automated systems based semiconductor sensor devices have been developed [9]- [13]. In agriculture industry, such technology is used to simplify the process for instance, nutrient prob [14] loose fruit picking machines [15], [16] and fruit sorting machines [17]- [19]. This automated system is powered by a microcontroller and Arduino.…”

Section: Introductionmentioning

confidence: 99%

Segregation of oil palm fruit ripeness using color sensor

Mustaffa

Arith

Peong

et al. 2022

IJEECS

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Oil palm is an important industry that has contributed to income and support to the economic sector especially for Malaysia and Indonesia. However, most of the equipment in the oil palm industry is still operated manually. This work developed a system to separate bunches of oil palm fruit using color sensors according to maturity level. Fruit color plays a decisive point in determining fruit maturity. Here, a specific threshold point of red green blue (RGB) was obtained for the determination of the maturity level of oil palm fruit. Point values of < 120, 120 < x < 150 and > 150 represent the maturity levels of unripe, under ripe and ripe, respectively. This paper is the first to report the RGB points for use in the development of automated oil palm segregation system in the oil palm plantation industry. Thus, this paper will pave the way in producing an accurate and reliable oil palm separation system, which in turn has a positive effect in reducing human error. In the future, a set of sensors is proposed to detect a bunch of the oil palm fruits. This further can speed up the segregation process and more suitable for adaptation to the industry.

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“…Mass and volume predictions from easily determined physical attributes may facilitate design and operation of post-harvest sorting machines and processes (Gonzalez et al 2017). Several researchers indicated that machine learning algorithms yielded better outcomes for prediction of fruit characteristics than the conventional methods (Moosavi and Sepaskha 2012;Demir et al 2017;Kus et al 2017;Shabani et al 2017;Çetin et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pistachio (Pistacia vera L.) Mass Based on Shape and Size Attributes by Using Machine Learning Algorithms

Sağlam

Çetin

2021

Food Anal. Methods

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Size, mass, and shape attributes play a significant role in the quality assessment and post-harvest technologies of agricultural products. Pistachio is widely consumed worldwide, and Turkey has 3rd place in world pistachio production. In this study, physical attributes of 6 different pistachio cultivars (Beyaz Ben, Keten gömleği, Kirmizi, Siirt, Tekin, Uzun) were determined and machine learning algorithms (Multilayer Perceptron (MLP), k-Nearest Neighbor (kNN), Random Forest (RF), Gaussian processes (GP)) were used for mass prediction of these pistachio cultivars. Siirt and Tekin cultivars had the greatest gravitational and dimensional attributes. Among the pistachio cultivars, Kirmizi and Uzun had the greatest shape index and elongation values. Keten gömleği and Beyaz cultivars had the lowest averages of mass and area attributes both for nuts and kernels. Kernel and nut mass of pistachio had significant correlations with volume, geometric mean diameter, and projected and surface area (p < 0.01). Present findings revealed that Gaussian processes had the greatest correlation coefficients (0.976 for nut mass and 0.948 for kernel mass prediction) and the lowest RMSE values (0.038 for nut and 0.029 for kernel mass prediction). This algorithm was respectively followed by Multilayer Perceptron and Random Forest algorithms. Present findings revealed that Gaussian processes, Multilayer Perceptron, and Random Forest algorithms could potentially be used for mass prediction of pistachio cultivars.

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Mass and Volume Estimation of Passion Fruit using Digital Images

Cited by 18 publications

References 13 publications

In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation

In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation

Segregation of oil palm fruit ripeness using color sensor

Prediction of Pistachio (Pistacia vera L.) Mass Based on Shape and Size Attributes by Using Machine Learning Algorithms

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