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Mature Tomato Fruit Detection algorithm Based on improved HSV and Watershed algorithm

IFAC-PapersOnLine 2018年第17期51卷 431-436页

作者： Malik, Muhammad Hammad Zhang, Ting Li, Han Zhang, Man Shabbir, Sana Saeed, Ahmed Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education China Agricultural University Haidian Beijing China Beijing Beida Software Engineering Co. Ltd. China College of Information and Electrical Engineering China Agricultural University HaidianBeijing China

Tomato is well known fruit since it has many essential and beneficial nutrients like antioxidant, vitamin C and A for human daily diet. Tomato picking by hand is both labor and time consuming. Therefore, to overcome these issues, tomato needs to be picked up automatically with the help of harvesting robot. Recently automation of fruit harvesting gains great popularity. To guide the harvesting robot to pick up the fruit correctly, it is important to correctly detect and find the location of the red mature fruit. In this study, a new mature tomato detection algorithm based on the improved HSV (Hue, Saturation, Value) color space and the improved watershed segmentation was proposed, by using a cheap and easily accessible RGB camera for guiding a robot to pick up red tomatoes automatically. For that purpose, the color images of tomato plants were captured under natural light. At the first stage, improved HSV transform was used to remove background and to detect only red tomatoes. Then the morphological operations were applied to modify the detected fruit. The second step was the separation of connected fruits, so that the harvesting robot can pick up fruit correctly. To achieve this task, the improved watershed algorithm was applied. As a result, the detection of separated red mature tomato was done by using the improved HSV and watershed, with uneven illumination and complex background. The overall red fruit detection accuracy was up to 81.6%, and it shows good potential to be used on harvesting robot. © 2018

关键词： Fruits Color Computer vision Error detection Harvesting Mathematical morphology Pickups Robots Signal detection Watersheds Detection accuracy Detection algorithm Harvesting robot Localization Morphological operations Uneven illuminations Water shed algorithm Watershed segmentation

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SRRT* - a probabilistic optimal trajectory planner for problematic area structures

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IFAC-PapersOnLine 2016年第30期49卷 331-336页

作者： Hess, Robin Jerg, Roland Lindeholz, Tobias Eck, Daniel Schilling, Klaus Zentrum fur Telematik e.V. Magdalene-Schoch-Straße 5 Würzburg97074 Germany CS VII Universty of Würzburg Am Hubland Würzburg97074 Germany

This paper extends the well known efficient RRT* algorithm to handle special environmental structures, such as narrow passages. By applying the watershed algorithm, inspired by image processing, a segmentation component is added to solve the given problem. Therefore the traditional watershed segmentation competes against the newly developed center marker watershed algorithm and the results of different experiments, combining both segmentation methods with the Rapidly Exploring Random Tree* (RRT*) algorithm to the Segmented Rapidly Exploring Random Tree* (SRRT*), are compared for several critical environments. It is shown, the whole trajectory planning process benefits from the segmentation and the SRRT*, using the new center marker watershed, outer performs the traditional RRT* method. © 2016

关键词： Motion planning Image segmentation Robot programming Trajectories Trees (mathematics) Autonomous Mobile Robot Critical environment Environmental structure Optimal trajectories Rapidly exploring random trees Segmentation methods Water shed algorithm Watershed segmentation

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