17 results on '"Santos, Filipe"'
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2. Monitoring System of an Industrial Steel Tower Structure
- Author
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Zeferino, João, Gonçalves, Eduardo, Carapito, Paulo, Santos, Filipe, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Rainieri, Carlo, editor, Fabbrocino, Giovanni, editor, Caterino, Nicola, editor, Ceroni, Francesca, editor, and Notarangelo, Matilde A., editor
- Published
- 2021
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3. Tomato Detection Using Deep Learning for Robotics Application
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Padilha, Tiago Cerveira, Moreira, Germano, Magalhães, Sandro Augusto, dos Santos, Filipe Neves, Cunha, Mário, Oliveira, Miguel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Marreiros, Goreti, editor, Melo, Francisco S., editor, Lau, Nuno, editor, Lopes Cardoso, Henrique, editor, and Reis, Luís Paulo, editor
- Published
- 2021
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4. Vineyard Segmentation from Satellite Imagery Using Machine Learning
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Santos, Luís, Santos, Filipe N., Filipe, Vitor, Shinde, Pranjali, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Moura Oliveira, Paulo, editor, Novais, Paulo, editor, and Reis, Luís Paulo, editor
- Published
- 2019
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5. In-Field Hyperspectral Proximal Sensing for Estimating Grapevine Water Status to Support Smart Precision Viticulture †.
- Author
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David, Erica, Tosin, Renan, Gonçalves, Igor, Rodrigues, Leandro, Barbosa, Catarina, Santos, Filipe, Pinheiro, Hugo, Martins, Rui, and Cunha, Mario
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GRAPES ,STANDARD deviations ,VITIS vinifera ,PRECISION farming ,VITICULTURE ,IRRIGATION management ,PRINCIPAL components analysis - Abstract
Predawn leaf water potential (Ψ
pd ) is the main parameter to determine plant water status, and it has been broadly used to support irrigation management. However, the Scholander pressure chamber methodology is laborious, time-consuming and invasive. This study examined a low-cost hyperspectral proximal sensor to estimate the Ψpd in grapevine (Vitis vinifera L.). For this, both the Ψpd and spectral reflectance (340–850 nm) were accessed in grapevines in a commercial vineyard located in the Douro Wine Region, northeast Portugal. A machine-learning algorithm was tested and validated to assess grapevine's water status. The experiment was performed in a randomized design with 12 grapevines (cv. Touriga Nacional) per irrigation treatment: non-irrigated, 30% crop evapotranspiration (Etc), and 60% Etc. The dataset was analyzed using Principal Component Analysis (PCA), and the machine-learning regression algorithm applied was Extreme Gradient Boosting (Xgboost). Results from the validation dataset (n = 108) for the Xgboost tested exhibited a root mean square error (RMSE) of 0.23 MPa, a mean absolute error (MAPE) of 16.57% and an R² value of 0.95. These results demonstrate that the hyperspectral sensor and Xgboost algorithm show potential for predicting the Ψpd in vineyards, regardless of a plant's water status. [ABSTRACT FROM AUTHOR]- Published
- 2023
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6. Enhancing Grape Brix Prediction in Precision Viticulture: A Benchmarking Study of Predictive Models Using Hyperspectral Proximal Sensors †.
- Author
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Santos-Campos, Maria, Tosin, Renan, Rodrigues, Leandro, Gonçalves, Igor, Barbosa, Catarina, Martins, Rui, Santos, Filipe, and Cunha, Mário
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PARTIAL least squares regression ,PREDICTION models ,SUSTAINABILITY ,MACHINE learning ,FOOD industry - Abstract
Sustainable and efficient agricultural production is a growing priority in modern society. Viticulture, an important agricultural and food sector, also faces this challenge. Precision Viticulture (PV) has gained prominence as it aims to foster high-quality, efficient, and environmentally sustainable practices. The Soluble Solids Content (SSC) is essential for assessing grape ripeness and quality in the winemaking process. Conventional methods for determining SSC values (expressed in °Brix) are invasive, expensive, and labour-intensive, necessitating sample preparation, making large-scale analysis impractical. In response to these limitations, this study presents an innovative approach within the field of Precision Viticulture. It focuses on the non-invasive prediction of SSC using low-cost proximal hyperspectral optical sensors. These sensors rely on spectral reflectance measurements in the range of 340–850 nm. This study was conducted in a commercial vineyard in the Demarcated Douro Region, Cima-Corgo sub-region, Portugal, over six weeks during ripening. In total, 169 grape berries from Touriga Nacional vines were analysed under three irrigation regimes (no irrigation, 30% ETc, and 60% ETc). After organising and preprocessing the data, machine learning algorithms, namely Partial Least Squares Regression (PLS), Random Forest (RF), and the Generalised Linear Model (GLM), were applied to predict SSC values. These models' performance was thoroughly evaluated using cross-validation techniques. The performance of different models was evaluated, showing significant differences according to the metrics used (R2, RMSE, and MAPE). The RF model demonstrated effectiveness and precision. A high R² value of 0.9312, coupled with low RMSE (0.9199 °Brix) and MAPE (3.88%), signifies a strong fit to the data and accurate predictive capabilities. The results of this benchmarking study on predictive models of SSC provide valuable insights into the performance of various models, aiding winegrowers and winemakers in decision making. [ABSTRACT FROM AUTHOR]
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- 2023
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7. Visual Signature for Place Recognition in Indoor Scenarios
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dos Santos, Filipe Neves, Costa, Paulo Cerqueira, Moreira, António Paulo, Moreira, António Paulo, editor, Matos, Aníbal, editor, and Veiga, Germano, editor
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- 2015
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8. Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling.
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Reis Pereira, Mafalda, Neves dos Santos, Filipe, Tavares, Fernando, and Cunha, Mário
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FISHER discriminant analysis ,BACTERIAL wilt diseases ,SUPERVISED learning ,BACTERIAL diseases ,MACHINE learning ,PREDICTION models ,CHERRIES ,TOMATOES - Abstract
Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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9. EyeLSD a Robust Approach for Eye Localization and State Detection
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Eddine, Benrachou Djamel, dos Santos, Filipe Neves, Boulebtateche, Brahim, and Bensaoula, Salah
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- 2017
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10. APPLICATION OF GRAPHICAL REPRESENTATIONS FROM VIDEO GAMES TO AMPLIFY CO-PRESENCE IN GROUP VISITS TO VIRTUAL HOUSES.
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Freitas, André, Santos, Filipe, and Santana, Pedro
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VIDEO games ,REAL estate business ,ARTIFICIAL intelligence ,MACHINE learning ,DECISION making - Abstract
In real life, the ability that people have of observing the elements of their surrounding space contributes for the sense of presence and social co-presence in that physical space. Digital avatars are often the employed means for providing a similar sense of co-presence in virtual environments. Digital avatars of the users and other co-presence amplification strategies (e.g., co-presence inducing graphical representations) have been extensively studied and employed in multi-player videogames. This paper studies the application of some these strategies to amplify the sense of co-presence of people in virtual group house visits, which is an application scenario with value for real estate industry and participatory design in architecture. For this purpose, a Unity-based collaborative virtual environment was implemented and tested in a user study with 33 participants. The obtained results show that the implemented strategies enrich the sense of co-presence in the virtual environment and trigger interesting collaborative interactions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
11. Deep Learning YOLO-Based Solution for Grape Bunch Detection and Assessment of Biophysical Lesions.
- Author
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Pinheiro, Isabel, Moreira, Germano, Queirós da Silva, Daniel, Magalhães, Sandro, Valente, António, Moura Oliveira, Paulo, Cunha, Mário, and Santos, Filipe
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DEEP learning ,GRAPES ,OBJECT recognition (Computer vision) ,BERRIES ,COMPUTER vision ,ECONOMIC activity - Abstract
The world wine sector is a multi-billion dollar industry with a wide range of economic activities. Therefore, it becomes crucial to monitor the grapevine because it allows a more accurate estimation of the yield and ensures a high-quality end product. The most common way of monitoring the grapevine is through the leaves (preventive way) since the leaves first manifest biophysical lesions. However, this does not exclude the possibility of biophysical lesions manifesting in the grape berries. Thus, this work presents three pre-trained YOLO models (YOLOv5x6, YOLOv7-E6E, and YOLOR-CSP-X) to detect and classify grape bunches as healthy or damaged by the number of berries with biophysical lesions. Two datasets were created and made publicly available with original images and manual annotations to identify the complexity between detection (bunches) and classification (healthy or damaged) tasks. The datasets use the same 10,010 images with different classes. The Grapevine Bunch Detection Dataset uses the Bunch class, and The Grapevine Bunch Condition Detection Dataset uses the OptimalBunch and DamagedBunch classes. Regarding the three models trained for grape bunches detection, they obtained promising results, highlighting YOLOv7 with 77% of mAP and 94% of the F1-score. In the case of the task of detection and identification of the state of grape bunches, the three models obtained similar results, with YOLOv5 achieving the best ones with an mAP of 72% and an F1-score of 92%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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12. Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae.
- Author
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Reis-Pereira, Mafalda, Tosin, Renan, Martins, Rui, Neves dos Santos, Filipe, Tavares, Fernando, and Cunha, Mário
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PSEUDOMONAS syringae ,ACTINIDIA ,MACHINE learning ,SIGNAL processing ,KIWIFRUIT ,PHYTOSANITATION ,ORCHARDS - Abstract
Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV–VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325–1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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13. EyeLSD a Robust Approach for Eye Localization and State Detection.
- Author
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Eddine, Benrachou, dos Santos, Filipe, Boulebtateche, Brahim, and Bensaoula, Salah
- Abstract
Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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14. Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato.
- Author
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Moreira, Germano, Magalhães, Sandro Augusto, Pinho, Tatiana, dos Santos, Filipe Neves, and Cunha, Mário
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DEEP learning ,TOMATO ripening ,GREENHOUSES ,COLOR space ,AGRICULTURAL productivity ,COMPUTER vision ,TOMATOES ,FRUIT ripening - Abstract
The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
15. Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse.
- Author
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Magalhães, Sandro Augusto, Castro, Luís, Moreira, Germano, dos Santos, Filipe Neves, Cunha, Mário, Dias, Jorge, Moreira, António Paulo, and Daras, Petros
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DEEP learning ,TOMATOES ,AGRICULTURAL robots ,ARTIFICIAL intelligence ,GREENHOUSE plants ,TOMATO harvesting ,SOLID state drives - Abstract
The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of 66.15 %, an mAP of 51.46 % and an inference time of 16.44 m s with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5 m s. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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16. Smartphone Applications Targeting Precision Agriculture Practices—A Systematic Review.
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Mendes, Jorge, Pinho, Tatiana M., Neves dos Santos, Filipe, Sousa, Joaquim J., Peres, Emanuel, Boaventura-Cunha, José, Cunha, Mário, and Morais, Raul
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MOBILE apps ,SENSE organs ,META-analysis ,AGRICULTURAL development ,MOBILE health ,PRECISION farming ,MOBILE computing - Abstract
Traditionally farmers have used their perceptual sensorial systems to diagnose and monitor their crops health and needs. However, humans possess five basic perceptual systems with accuracy levels that can change from human to human which are largely dependent on the stress, experience, health and age. To overcome this problem, in the last decade, with the help of the emergence of smartphone technology, new agronomic applications were developed to reach better, cost-effective, more accurate and portable diagnosis systems. Conventional smartphones are equipped with several sensors that could be useful to support near real-time usual and advanced farming activities at a very low cost. Therefore, the development of agricultural applications based on smartphone devices has increased exponentially in the last years. However, the great potential offered by smartphone applications is still yet to be fully realized. Thus, this paper presents a literature review and an analysis of the characteristics of several mobile applications for use in smart/precision agriculture available on the market or developed at research level. This will contribute to provide to farmers an overview of the applications type that exist, what features they provide and a comparison between them. Also, this paper is an important resource to help researchers and applications developers to understand the limitations of existing tools and where new contributions can be performed. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
17. Smart And Cost-Effective Manipulator's End-Effector For Tomato Harvesting
- Author
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Francisco Adrião Silva Oliveira, Santos, Filipe Baptista Neves dos, Magalhães, Sandro Augusto Costa, Tinoco, Vítor Daniel Veloso, and Faculdade de Engenharia
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Machine Learning ,Electrical engineering, Electronic engineering, Information engineering ,Engenharia electrotécnica, electrónica e informática ,Engenharia electrotécnica, electrónica e informática [Ciências da engenharia e tecnologias] ,Robotics ,Agricultural Robotics ,Electrical engineering, Electronic engineering, Information engineering [Engineering and technology] - Abstract
There has been an increase in the variety of harvesting manipulators developed. However, sometimes the lack of efficiency of these manipulators, when compared with harvesting tasks performed by humans, makes it difficult to be used as a solution to the lack of labour available or as a tool to help with repetitive tasks. One of the key components of these manipulators is the end-effector, responsible for picking the fruits from the plant. This dissertation studies the design and making of an end-effector capable of harvesting tomato to be included on a custom Selective Compliance Assembly Robot Arm (SCARA) manipulator. To achieve the objective of developing the end-effector, this dissertation work contributes with: A systematic review about end-effectors already available. An open source design for an end-effector called FruitGrip. Test and validation of the tool on doing harvesting tasks in a laboratory with an artifical environment.
- Published
- 2022
- Full Text
- View/download PDF
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