Sensing and End-Effector for a Robotic Tomato Harvester

Ling, Peter P.; Ehsani, Reza; Ting, K. C.; Tseh, Yu; Ramalingam, Nagarajan; Klingman, Michael H.; Draper, Craig

doi:10.13031/2013.16727

Cited by 15 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sakai et al [31] provided designs based on parallel type manipulation for heavy material handling manipulator in agriculture such as watermelon, pumpkins, cabbage and lettuce. Ling et al [32] developed a four-finger prosthetic hand and embedded hand controller for tomato harvesting robot. The sensing and picking were 95% and 85%, respectively, compared with a previous prototype.…”

Section: Picking Systemmentioning

confidence: 99%

Deliberation on Design Strategies of Automatic Harvesting Systems: A Survey

Bachche

2015

Robotics

View full text Add to dashboard Cite

In Asia, decreasing farmer and labor populations due to various factors is a serious problem that leads to increases in labor costs, higher harvesting input energy consumption and less resource utilization. To solve these problems, researchers are engaged in providing long term and low-tech alternatives in terms of mechanization and automation of agriculture by way of efficient, low cost and easy to use solutions. This paper reviews various design strategies in recognition and picking systems, as well as developments in fruit harvesting robots during the past 30 years in several countries. The main objectives of this paper are to gather all information on fruit harvesting robots; focus on the technical developments so far achieved in picking devices; highlight the problems still to be solved; and discuss the future prospects of fruit harvesting robots.

show abstract

Section: Picking Systemmentioning

confidence: 99%

Deliberation on Design Strategies of Automatic Harvesting Systems: A Survey

Bachche

2015

Robotics

View full text Add to dashboard Cite

show abstract

“…Tillett (1993) reviewed several prototype robots and clarified the importance of the manipulator design and its application to practical use. Several researchers have applied robotic technology to fields in greenhouses; For example, Ling et al (2004) for tomatoes, Muscato et al (2005) for oranges, Edan et al (2000) for melons and Van Henten et al (2002) for cucumbers. A thorough review with regard to fruit recognition systems can be found in Jimenez et al (2000).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Machine Vision System for Ripe Fruit Detection Based on Robotic Harvesting

Thendral¹,

Suhasini²

2015

AJFST

View full text Add to dashboard Cite

The proposed study intends to provide an efficient algorithm for the instruction of an automatic robot arm to choose the ripe fruits on the tree. Steps involved in this study are recognizing and locating the ripe fruits from the leaf and branch portions by using an efficient machine vision algorithm. Initially, discrete wavelet transform is used for better preserving of edges and fine details in the given input image. Then RGB, HSV, L*a*b* and YIQ color spaces were studied to segment the ripe fruits from the surrounding objects. Finally, the results showed that 'I' component of the YIQ color space has the best criterion for recognizing the fruit from the foliage. The fruit segmentation based on machine vision has an occlusion problem. In this proposed method these problems are also examined.

show abstract

“…Machine vision is commonly used as an automatic nondestructive visual inspection tool (Aleixos et al, 2002), especially in the production of delicate fruit such as citrus, blueberries, apples, and tomatoes. Recently, many studies using machine vision techniques have been reported for sorting fruit and yield estimation, including fruit detection and mass estimation (Yang et al, 2012), disease detection (Li et al, 2014;Pourreza et al, 2013), and robotic harvesting (Ling et al, 2004). For fruit detection and mass estimation using a closed imaging system, several machine vision applications have been studied.…”

mentioning

confidence: 99%

A Machine Vision System for Quantification of Citrus Fruit Dropped on the Ground under the Canopy

et al. 2015

View full text Add to dashboard Cite

The overall goal of this study was to develop a machine vision system to quantify dropped citrus fruits on the ground. Specific objectives were: (1) to build a machine vision system suitable for citrus grove field conditions, (2) to develop an image enhancement algorithm for varying illumination conditions, and (3) to develop an image processing algorithm to estimate citrus fruit drop count and mass. The image processing algorithm consisted of (1) illumination enhancement using a retinex algorithm, (2) classification, (3) segmentation using a watershed algorithm with h-minima transform, and (4) ellipse fitting for mass estimation. Performances of the algorithms were evaluated in terms of correct identification and false positive errors. The average correct identification rate was 88.1%, 83.6%, and 82.9% for logistic regression, k-nearest neighbor (kNN), and Bayesian classifiers, respectively. False positive errors were 13.7%, 40.9%, and 17.9% for logistic regression, kNN, Bayesian classifiers, respectively. The results demonstrate the system's ability to quantify dropped fruits with specific geo-referenced location information. Spatially varied fruit drop maps plotted from the results can assist growers in finding problematic areas in their citrus groves more efficiently while reducing inspection and treatment costs. Such maps can also facilitate treatment of citrus Huanglongbing (HLB) disease in combination with HLB intensity data, psyllid counts, fertilization programs, and other block-specific management practices.

show abstract

Sensing and End-Effector for a Robotic Tomato Harvester

Cited by 15 publications

References 0 publications

Deliberation on Design Strategies of Automatic Harvesting Systems: A Survey

Deliberation on Design Strategies of Automatic Harvesting Systems: A Survey

Enhanced Machine Vision System for Ripe Fruit Detection Based on Robotic Harvesting

A Machine Vision System for Quantification of Citrus Fruit Dropped on the Ground under the Canopy

Contact Info

Product

Resources

About