Grape stem detection using regression convolutional neural networks

Kalampokas, Τheofanis; Vrochidou, Εleni; Papakostas, George A.; Pachidis, Theodore; Kaburlasos, Vassilis G.

doi:10.1016/j.compag.2021.106220

Cited by 29 publications

(14 citation statements)

References 46 publications

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“…The models were assessed for their performance by testing if pearl chain arrangements in a micrographs can be correlated to input voltages to find the model with the best performance using these four key performance metrics: Mean Absolute Error (MAE), Mean Relative Error (MRE), Mean Squared Error (MSE), R-squared, and Root Mean Square Error (RMSE) 48 , 49 , 57 , 79 . They are mathematically expressed as given by Eqs.…”

Section: Resultsmentioning

confidence: 99%

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

Ajala

Jalajamony

Nair

et al. 2022

Sci Rep

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An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices.

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Section: Resultsmentioning

confidence: 99%

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

Ajala

Jalajamony

Nair

et al. 2022

Sci Rep

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“…In the context of this work, overall system performance results are not reported. However, this work references results of individual methodologies that implement basic tasks of the proposed system, such as the remote-control unit function [31], the navigation route mapping [30], the kinematic analysis of the robotic arm [43] and machine vision algorithms for detection of grape clusters and leaves [33], stems [34], harvest crates [35], vine trunks [36] and grape ripeness level [37]. These results will be validated in real scenarios covering a predefined range of vineyards of three well-known Northern Greece wine producers as part of a national research program [29].…”

Section: Discussionmentioning

confidence: 99%

“…(2) grape stem detection [34], (3) harvest crate detection [35], (4) grapevine trunk detection [36], (5) ripeness estimation and yield time prediction [37] and ( 6) grapes defect detection.…”

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confidence: 99%

An Autonomous Grape-Harvester Robot: Integrated System Architecture

et al. 2021

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This work pursues the potential of extending “Industry 4.0” practices to farming toward achieving “Agriculture 4.0”. Our interest is in fruit harvesting, motivated by the problem of addressing the shortage of seasonal labor. In particular, here we present an integrated system architecture of an Autonomous Robot for Grape harvesting (ARG). The overall system consists of three interdependent units: (1) an aerial unit, (2) a remote-control unit and (3) the ARG ground unit. Special attention is paid to the ARG; the latter is designed and built to carry out three viticultural operations, namely harvest, green harvest and defoliation. We present an overview of the multi-purpose overall system, the specific design of each unit of the system and the integration of all subsystems. In addition, the fully sensory-based sensing system architecture and the underlying vision system are analyzed. Due to its modular design, the proposed system can be extended to a variety of different crops and/or orchards.

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“…Kalampokas et al developed an autonomous grape harvesting robot that can reduce harvesting time by detecting grape stems in images. For this purpose, a regression convolutional neural network was deployed for finishing a stem segmentation task, which provides higher correct identification rates in highly changing environments [16]. Williams et al introduced a new multi-arm kiwifruit harvesting robot working in pergola style orchards.…”

Section: Introductionmentioning

confidence: 99%

A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking

Wang

Lin

et al. 2022

Agronomy

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For the automated robotic picking of bunch-type fruit, the strategy is to roughly determine the location of the bunches, plan the picking route from a remote location, and then locate the picking point precisely at a more appropriate, closer location. The latter can reduce the amount of information to be processed and obtain more precise and detailed features, thus improving the accuracy of the vision system. In this study, a long–close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera. The YOLOv5 object detection network and DBSCAN point cloud clustering method were used to determine the location of bunch fruits at a long distance to then deduce the sequence of picking. After reaching the close-distance position, the Mask RCNN instance segmentation method was used to segment the more distinctive bifurcate stems in the field of view. By processing segmentation masks, a dual reference model of “Point + Line” was proposed, which guided picking by the robotic arm. Compared with existing studies, this strategy took into account the advantages and disadvantages of depth cameras. By experimenting with the complete process, the density-clustering approach in long distance was able to classify different bunches at a closer distance, while a success rate of 88.46% was achieved during fruit-bearing branch locating. This was an exploratory work that provided a theoretical and technical reference for future research on fruit-picking robots.

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Grape stem detection using regression convolutional neural networks

Cited by 29 publications

References 46 publications

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

An Autonomous Grape-Harvester Robot: Integrated System Architecture

A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking

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