Lézerszkenner alapú almadetektálás

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Classification of Fruit Ripeness with Model Descriptor Using Vgg 16 Architecture

The quality of the fruit is largely determined by the level of ripeness contained by the fruit itself. Until now, determining the level of fruit maturity is still done manually, as a result there are differences in perceptions in determining the level of fruit maturity. Therefore we need a system that is able to classify fruit maturity automatically. This research was conducted on 4 objects, namely apples, oranges, mangoes, and tomatoes. The training was conducted with split data with a presentation 70:20:10 based on 4 test scenarios, the data was converted to RGB to L * a * b first and some were not converted and were immediately trained using CNN VGG16 with the transfer learning method where fine tuning would be done on the layer block 5 and modification of the classification layer using the Multi-SVM classifier. The highest accurasi reach 92% at scenario 4 with 90 data per class

Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network

In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method

Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

In this letter, we present a new robotic harvester (Harvey) that can autonomously harvest sweet pepper in protected cropping environments. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. Initial field trials in protected cropping environments, with two cultivar, demonstrate the efficacy of this approach achieving a 46% success rate for unmodified crop, and 58% for modified crop. Furthermore, for the more favourable cultivar we were also able to detach 90% of sweet peppers, indicating that improvements in the grasping success rate would result in greatly improved harvesting performance

Toward a New Approach in Fruit Recognition using Hybrid RGBD Features and Fruit Hierarchy Property

We present hierarchical multi-feature classification (HMC) system for multiclass fruit recognition problem. Our approach to HMC exploits the advantages of combining multimodal features  and  the  fruit  hierarchy  property.  In  the construction of hybrid features, we take the advantage of using color feature in the fruit recognition problem and combine it with 3D shape feature of depth channel of RGBD (Red, Green, Blue, Depth) images. Meanwhile, given a set of fruit species and variety, with a preexisting hierarchy among them, we consider the problem of assigning images to one of these fruit variety from the point of view of a hierarchy. We report on computational experiment using this approach. We show that the use of hierarchy structure along with hybrid RGBD features can improve the classification performance

design and simulation of two robotic systems for automatic artichoke harvesting

The target of this research project was a feasibility study for the development of a robot for automatic or semi-automatic artichoke harvesting. During this project, different solutions for the mechanical parts of the machine, its control system and the harvesting tools were investigated. Moreover, in cooperation with the department DISPA of University of Catania, different field structures with different kinds of artichoke cultivars were studied and tested. The results of this research could improve artichoke production for preserves industries. As a first step, an investigation on existing machines has been done. From this research, it has been shown that very few machines exist for this purpose. Based also on previous experiences, some proposals for different robotic systems have been done, while the mobile platform itself was developed within another research project. At the current stage, several different configurations of machines and harvesting end-effectors have been designed and simulated using a 3D CAD environment interfaced with Matlab®. Moreover, as support for one of the proposed machines, an artificial vision algorithm has been developed in order to locate the artichokes on the plant, with respect to the robot, using images taken with a standard webcam

Lézerszkenner alapú almadetektálás = Laser based apple fruit detection

A minőségi almatermesztés fontos technológiai eleme a gyümölcs betakarítás előtti információk beszerzése, különösen a termés mennyiségének meghatározása, amely alapvetően meghatározhatja a gazdaságosságát és a betakarítását követő, ún. post harvest tevékenységeket (pl. tárolás, logisztika, marketing, stb.J. A becslésekre alapozott termésmennyiség meghatározása sokszor igen nagy hibákkal terhelt a kertészeti gyakorlatban. Az informatikai rendszerek fejlődése mára lehetővé teszik a gyümölcsök detektálását. Kutatásaink során a betakarítást megelőzően, 3D lézerszkenner segítségével mértük fel egy intenzív almaültetvény egy részét a Debreceni Egyetem, ATK, Debreceni Tangazdasága és Tájkutató Intézete, Pallagi Génbank és Gyakorlóhelyén. A nagy pontosságú lézeres adatok lehetővé tették a gyümölcsök pozíciójának és mértének megismerését

Autonomous Fruit Harvester with Machine Vision

This study presents an autonomous fruit harvester with a machine vision capable of detecting and picking or cutting an orange fruit from a tree. The system of is composed of a six-degrees of freedom (6-DOF) robotic arm mounted on a four-wheeled electric kart. The kart uses ZED stereo camera for depth estimation of a target. It can also be used to detect trees using the green detection algorithm. Image processing is done using Microsoft Visual Studio and OpenCV library. The x & y coordinates and distance of the tree are passed on to Arduino microcontroller as inputs to motor control of the wheels. When the kart is less than 65cm to the tree, the kart stops and the robotic arm system takes over to search and harvest orange fruits. The robotic arm has a webcam and ultrasonic sensor attached at its end-effector. The webcam is used for orange fruit detection while ultrasonic sensor is used to provide feedback on the distance of the orange fruit to end-effector. Multiple fruit harvesting is successfully done. The success rate of harvesting and putting fruit into the basket is 80% and 85% for the gripper end-effector and cutter end-effector respectively

Classificació automàtica de fruites utilitzant tècniques d'aprenentatge profund

The productivity of the agri-food sector experiences continuous and growing challenges that make the use of innovative technologies to maintain and even improve their competitiveness a priority. One way to achieve this goal is the development of flexible and portable systems capable of obtaining 2D/3D measurements and classifying objects based on color and depth images taken from multiple sensors. In this project, deep learning methods for fruit detection and classification will be explored.És crucial disposar de sistemes de detecció d'objectes precisos i fiables per a desenvolupar feines d'alt nivell en agricultura com serien fer un mapatge del camp o robotitzar les collites. Aquest document utilitza una Faster-RCNN -que consisteix xarxa de detecció d'objectes de l'estat de l'art- orientada a la detecció de fruites que en aquest treball només seran pomes. La xarxa serà introduïda i explicada. Es fa un anàlisis d'obtenció dels paràmetres d'entrenament i diversos experiments orientats a maximitzar la finesa (accuracy en anglès) del model que es vol obtenir. La xarxa neuronal estarà consistirà en una part preentrenada una part completament per entrenar. Aquest estudi no ha aconseguit equiparar els resultats de treballs anteriors (F1 score > 0.9) però tampoc es pot dir que hagi obtingut mals resultats, com seria un F1-score de 0.85