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Al422b Epub

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The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm.

The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table LUT defined in the RGB color space and optimized for fruit picking.

Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches.

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The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second. Keywords: real-time embedded system, autonomous system, fruit detection, LUT models 1.

Introduction In recent years, machine vision has been used in many agricultural applications that require visual inspection and color grading systems. In such an environment, the development of automatic harvesting machines specialized in fresh fruit is expected to have a large impact in the local and national economy by contributing to reducing the fruit production costs and also increasing the quality of the harvesting process. The development of suitable harvesting machines requires the combination of highly robotized picking machines with highly portable and compact autonomous vision systems to guide the automatic selection and picking of the optimal fruit [ 2 , 3 ].

The new contribution of this work is to propose the development of an autonomous real-time embedded vision system for fast fruit detection and tracking in acquired RGB color orchard images. The design and development of the embedded system is inspired in [ 4 ] and based on the preliminary work performed in [ 5 ].

In the future, this embedded system will be placed directly in a gripper tool of a robotized arm designed for automatic harvesting and the fully autonomous machine will be able to operate different arms simultaneously. Additionally, this work proposed the use of a three-dimensional look-up-table LUT defined in the RGB color space to perform fruit detection and tracking in the embedded vision system.

Two methodologies were proposed and tested to create and optimize the LUT for the case of detecting and tracking red peaches: one based on 3D linear color models [ 6 ] and the other based on a 3D histogram [ 7 ]. At this moment, both methodologies proposed to create the LUTs used in the proposed embedded system require the manual selection of different image features, such as individual fruit and leaves in different orchard images, an aspect that must be automated in the future.

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However, once created, a 3D LUT defined in the RGB color space enables very fast fruit segmentation just by using the RGB pixel color intensities as table indices of the LUT because the value read from the table is directly the segmentation result. Background The background of this work refers mainly to the design of embedded vision systems and the application and benefits of the use of LUTs for image segmentation. Embedded Vision Systems Recent advances in computers and the improvement in micro-fabrication technology have fostered the design of tiny embedded systems with onboard electronics and sensors that also incorporates image-processing capabilities.

For example, in [ 4 ], a versatile low cost embedded vision platform was presented to find blobs of a specific color in an image and also perform JPEG compression, frame differentiation, edge detection, image convolution, face detection, and color histogram computation. This embedded system was based on a low-cost CMOS color camera module, a frame buffer chip, and a low-cost microcontroller that processes all the images acquired and which was the inspiration for this work.

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These camera applications can involve security, surveillance, photography, toys, robots, and drones. In image-processing applications, LUTs are mostly used in the recognition of colored objects and are widely applied in embedded vision systems.

Introduction In recent years, machine vision has been used in many agricultural applications that require visual inspection and color grading systems.

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