Identifying immature and mature pomelo fruits in trees by elliptical model fitting in the Cr–Cb color space

Liu, Tianhu; Ehsani, Reza; Toudeshki, Arash; Zou, Xiangjun; Wang, Hongjun

doi:10.1007/s11119-018-9586-1

Cited by 31 publications

(12 citation statements)

References 17 publications

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

Section: Introductionmentioning

confidence: 99%

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

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

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Section: Introductionmentioning

confidence: 99%

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

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“…The results showed that the recognition rate of apples can reach over 90%. Liu et al (2019) proposed that under Y'cbcr color space, a visual system was designed by using mathematical models such as elliptic boundary model and regional opening mathematical morphology model to judge whether pomelo was mature or not, and the total accuracy of the algorithm reached 93.5%. Arad et al (2019) proposed the controlled illumination acquisition protocol for flash-no-flash (FNF).…”

Section: Object Recognition Methods For Harvesting Robot Single Feature Vision Methods and Improvementmentioning

confidence: 99%

“…These objects vary in size, shape, color, and texture. The background and illumination of the crops vary continuously (Bulanon et al, 2010;Zhao et al, 2011;Qingchun et al, 2012;Hemming et al, 2014;Silwal et al, 2016;Liu et al, 2019;Zhuang et al, 2019). Machine vision-based harvesting robots should have the ability to sense and adapt to different crop types or environmental changes Silwal et al, 2017), collect information, detect targets, and learn autonomously.…”

Section: Visual Harvesting Robotmentioning

confidence: 99%

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

et al. 2020

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The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.

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“…Traditional pomelo internal quality detection methods include sensory [6] and physicochemical index methods [7,8], both of which are time-and labor-intensive, damage and waste fruit, and cannot be applied to detect and guarantee the quality of every postharvest pomelo. Machine vision has been widely applied as a fast, intelligent, and nondestructive method for grading fruit quality [9,10], but this only provides information concerning external characteristics.…”

Section: Introductionmentioning

confidence: 99%

Rapid Nondestructive Detection of Water Content and Granulation in Postharvest “Shatian” Pomelo Using Visible/Near-Infrared Spectroscopy

Ference

et al. 2020

Biosensors

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Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small, thin-peeled fruit, though less so for large, thick-peeled fruit due to a weak spectral signal resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. “Shatian” pomelo is a traditional Chinese large, thick-peeled fruit, and granulation and water loss are two major internal quality factors that influence its storage quality. However, there is no efficient, nondestructive detection method for measuring these factors. Thus, the VIS/NIR spectral signal detection of 120 pomelo samples during storage was performed. Information mining (singular sample elimination, data processing, feature extraction) and modeling were performed in different ways to construct the optimal method for achieving an accurate detection. Our results showed that the water content of postharvest pomelo was optimally detected using the Savitzky–Golay method (SG) plus the multiplicative scatter correction method (MSC) for data processing, genetic algorithm (GA) for feature extraction, and partial least squares regression (PLSR) for modeling (the coefficient of determination and root mean squared error of the validation set were 0.712 and 0.0488, respectively). Granulation degree was best detected using SG for data processing and PLSR for modeling (the detection accuracy of the validation set was 100%). Additionally, our research showed a weak relationship between the pomelo water content and granulation degree, which provided a reference for the existing debates. Therefore, our results demonstrated that VIS/NIR combined with optimal information mining and modeling methodswas feasible for determining the water content and granulation degree of postharvest pomelo, and for providing references for the nondestructive internal quality detection of other large, thick-peeled fruits.

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Identifying immature and mature pomelo fruits in trees by elliptical model fitting in the Cr–Cb color space

Cited by 31 publications

References 17 publications

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

Rapid Nondestructive Detection of Water Content and Granulation in Postharvest “Shatian” Pomelo Using Visible/Near-Infrared Spectroscopy

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