{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T07:02:15Z","timestamp":1777100535845,"version":"3.51.4"},"reference-count":62,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Agriculture"],"abstract":"<jats:p>Pruning wood mass is crucial for grapevine management, as it reflects the vine\u2019s vigor and balance. However, traditional manual measurement methods are time-consuming and labor-intensive. Recent advances in digital imaging offer non-invasive techniques, but limited research has explored pruning wood weight estimation, especially regarding the use of artificial backgrounds and lighting. This study assesses the use of image analysis for estimating wood weight, focusing on image acquisition conditions. This research aimed to (i) evaluate the necessity of artificial backgrounds and (ii) identify optimal daylight conditions for accurate image capture. Results demonstrated that estimation accuracy strongly depends on the sun\u2019s position relative to the camera. The highest accuracy was achieved when the camera faced direct sunlight (morning on the northwest canopy side and afternoon on the southeast side), with R2 values reaching 0.90 and 0.93, and RMSE as low as 44.24 g. Artificial backgrounds did not significantly enhance performance, suggesting that the method is applicable under field conditions. Leave-One-Group-Out Cross-Validation (LOGOCV) confirmed the model\u2019s robustness when applied to Catarratto cv. (LOGOCV R2 = 0.86 in NB and 0.84 in WB), though performance varied across other cultivars. These findings highlight the potential of automated image-based assessment for efficient vineyard management, using minimal effort adjustments to image collection that can be incorporated into low-cost setups for pruning wood weight estimation.<\/jats:p>","DOI":"10.3390\/agriculture15090966","type":"journal-article","created":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T20:57:16Z","timestamp":1746392236000},"page":"966","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Estimating Pruning Wood Mass in Grapevine Through Image Analysis: Influence of Light Conditions and Acquisition Approaches"],"prefix":"10.3390","volume":"15","author":[{"given":"Stefano","family":"Puccio","sequence":"first","affiliation":[{"name":"Department of Agricultural, Food and Forest Sciences (SAAF), University of Palermo, Viale delle Scienze, 90128 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4930-3849","authenticated-orcid":false,"given":"Daniele","family":"Miccich\u00e8","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Food and Forest Sciences (SAAF), University of Palermo, Viale delle Scienze, 90128 Palermo, Italy"}]},{"given":"Gon\u00e7alo","family":"Victorino","sequence":"additional","affiliation":[{"name":"Linking Landscape, Environment, Agriculture and Food (LEAF), Instituto Superior de Agronomia, Universidade de Lisboa, 1349-017 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2456-1200","authenticated-orcid":false,"given":"Carlos Manuel","family":"Lopes","sequence":"additional","affiliation":[{"name":"Linking Landscape, Environment, Agriculture and Food (LEAF), Instituto Superior de Agronomia, Universidade de Lisboa, 1349-017 Lisboa, Portugal"}]},{"given":"Rosario","family":"Di Lorenzo","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Food and Forest Sciences (SAAF), University of Palermo, Viale delle Scienze, 90128 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0040-5035","authenticated-orcid":false,"given":"Antonino","family":"Pisciotta","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Food and Forest Sciences (SAAF), University of Palermo, Viale delle Scienze, 90128 Palermo, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1111\/j.1755-0238.2000.tb00160.x","article-title":"Growth and Dry Matter Partitioning of Pinot Noir (Vitis vinifera L.) in Relation to Leaf Area and Crop Load","volume":"6","author":"Petrie","year":"2000","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.5344\/ajev.2001.52.3.165","article-title":"Sustainable Grape Productivity and the Growth-Yield Relationship: A Review","volume":"52","author":"Howell","year":"2001","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1111\/j.1755-0238.2000.tb00161.x","article-title":"Fruit Composition and Ripening of Pinot Noir (Vitis vinifera L.) in Relation to Leaf Area","volume":"6","author":"Petrie","year":"2000","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_4","unstructured":"Smart, R., and Robinson, M. (1991). Sunlight into Wine: A Handbook for Winegrape Canopy Management, Winetitles."},{"key":"ref_5","unstructured":"Viala, P., and Ravaz, L. (1908). American Vines (Resistant Stock): Their Adaptation, Culture, Grafting and Propagation, Press of Freygang-Leary."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tomasi, D., Gaiotti, F., Petoumenou, D., Lovat, L., Belfiore, N., Boscaro, D., and Mian, G. (2020). Winter Pruning: Effect on Root Density, Root Distribution and Root\/Canopy Ratio in Vitis vinifera Cv. Pinot Gris. Agronomy, 10.","DOI":"10.20944\/preprints202008.0287.v1"},{"key":"ref_7","unstructured":"Palliotti, A., Poni, S., and Silvestroni, O. (2018). Manuale di Viticoltura, Edagricole."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.scienta.2016.03.046","article-title":"Mechanical Winter Pruning of Grapevine: Physiological Bases and Applications","volume":"204","author":"Poni","year":"2016","journal-title":"Sci. Hortic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"170","DOI":"10.5344\/ajev.2005.56.2.170","article-title":"Leaf Area\/Crop Weight Ratios of Grapevines: Influence on Fruit Composition and Wine Quality","volume":"56","author":"Kliewer","year":"2005","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s10658-012-0111-5","article-title":"Impacts of Plant Growth and Architecture on Pathogen Processes and Their Consequences for Epidemic Behaviour","volume":"135","author":"Calonnec","year":"2013","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pisciotta, A., Di Lorenzo, R., Novara, A., Laudicina, V.A., Barone, E., Santoro, A., Gristina, L., and Barbagallo, M.G. (2021). Cover Crop and Pruning Residue Management to Reduce Nitrogen Mineral Fertilization in Mediterranean Vineyards. Agronomy, 11.","DOI":"10.3390\/agronomy11010164"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"279","DOI":"10.5344\/ajev.2021.20042","article-title":"Comparison of Different Vegetative Indices for Calibrating Proximal Canopy Sensors to Grapevine Pruning Weight","volume":"72","author":"Taylor","year":"2021","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1111\/j.1755-0238.2003.tb00267.x","article-title":"Grapevine Dormant Pruning Weight Prediction Using Remotely Sensed Data","volume":"9","author":"Dobrowski","year":"2003","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.foodchem.2018.11.140","article-title":"Precision Viticulture and Advanced Analytics. A Short Review","volume":"279","author":"Santesteban","year":"2019","journal-title":"Food Chem."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1111\/j.1755-0238.2011.00136.x","article-title":"Variation in Vine Vigour, Grape Yield and Vineyard Soils and Topography as Indicators of Variation in the Chemical Composition of Grapes, Wine and Wine Sensory Attributes","volume":"17","author":"Bramley","year":"2011","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Urretavizcaya, I., Miranda, C., Royo, J.B., and Santesteban, L.G. (2015). Within-Vineyard Zone Delineation in an Area with Diversity of Training Systems and Plant Spacing Using Parameters of Vegetative Growth and Crop Load. Precision Agriculture \u201915, Wageningen Academic Publishers.","DOI":"10.3920\/978-90-8686-814-8_59"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s11119-016-9450-0","article-title":"Relevance of Sink-Size Estimation for within-Field Zone Delineation in Vineyards","volume":"18","author":"Urretavizcaya","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.compag.2015.10.009","article-title":"Grapevine Flower Estimation by Applying Artificial Vision Techniques on Images with Uncontrolled Scene and Multi-Model Analysis","volume":"119","author":"Aquino","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"229","DOI":"10.5344\/ajev.2015.15037","article-title":"Assessment of Vineyard Canopy Porosity Using Machine Vision","volume":"67","author":"Diago","year":"2016","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_20","first-page":"107","article-title":"The Effect of Alternative Pruning Methods on the Viticultural and Oenological Performance of Some Wine Grape Varieties","volume":"28","author":"Archer","year":"2007","journal-title":"S. Afr. J. Enol. Vitic."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1146\/annurev-arplant-050312-120137","article-title":"Future Scenarios for Plant Phenotyping","volume":"64","author":"Fiorani","year":"2013","journal-title":"Annu. Rev. Plant Biol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mohimont, L., Alin, F., Rondeau, M., Gaveau, N., and Steffenel, L.A. (2022). Computer Vision and Deep Learning for Precision Viticulture. Agronomy, 12.","DOI":"10.3390\/agronomy12102463"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"\u00cd\u00f1iguez, R., Palacios, F., Barrio, I., Hern\u00e1ndez, I., Guti\u00e9rrez, S., and Tardaguila, J. (2021). Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards. Agronomy, 11.","DOI":"10.3390\/agronomy11051003"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.compag.2017.11.026","article-title":"Automated Early Yield Prediction in Vineyards from On-the-Go Image Acquisition","volume":"144","author":"Aquino","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1002\/jsfa.7797","article-title":"Image Analysis-based Modelling for Flower Number Estimation in Grapevine","volume":"97","author":"Millan","year":"2017","journal-title":"J. Sci. Food Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105796","DOI":"10.1016\/j.compag.2020.105796","article-title":"Automated Grapevine Flower Detection and Quantification Method Based on Computer Vision and Deep Learning from On-the-Go Imaging Using a Mobile Sensing Platform under Field Conditions","volume":"178","author":"Palacios","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1002\/jsfa.6512","article-title":"Assessment of Flower Number per Inflorescence in Grapevine by Image Analysis under Field Conditions","volume":"94","author":"Diago","year":"2014","journal-title":"J. Sci. Food Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"833","DOI":"10.20870\/oeno-one.2020.54.4.3616","article-title":"Yield Components Detection and Image-Based Indicators for Non-Invasive Grapevine Yield Prediction at Different Phenological Phases","volume":"54","author":"Victorino","year":"2020","journal-title":"OENO One"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"209","DOI":"10.20870\/oeno-one.2021.55.4.4741","article-title":"Grapevine Bunch Weight Estimation Using Image-Based Features: Comparing the Predictive Performance of Number of Visible Berries and Bunch Area","volume":"55","author":"Lopes","year":"2021","journal-title":"OENO One"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, L., Tang, Y., Zou, X., Wang, C., Zhang, P., and Feng, W. (2016). Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components. Sensors, 16.","DOI":"10.3390\/s16122098"},{"key":"ref_31","unstructured":"Casser, V. (2016, January 25). Using Feedforward Neural Networks for Color Based Grape Detection in Field Images. Proceedings of the CSCUBS, Computer Science Conference for University of Bonn Students, Bonn, Germany."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"16988","DOI":"10.3390\/s121216988","article-title":"Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions","volume":"12","author":"Diago","year":"2012","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1111\/j.1755-0238.2004.tb00022.x","article-title":"Yield Prediction from Digital Image Analysis: A Technique with Potential for Vineyard Assessments Prior to Harvest","volume":"10","author":"Dunn","year":"2004","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"De Bei, R., Fuentes, S., Gilliham, M., Tyerman, S., Edwards, E., Bianchini, N., Smith, J., and Collins, C. (2016). VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine. Sensors, 16.","DOI":"10.3390\/s16040585"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gatti, M., Dosso, P., Maurino, M., Merli, M.C., Bernizzoni, F., Jos\u00e9 Pirez, F., Plat\u00e8, B., Bertuzzi, G.C., and Poni, S. (2016). MECS-VINE\u00ae: A New Proximal Sensor for Segmented Mapping of Vigor and Yield Parameters on Vineyard Rows. Sensors, 16.","DOI":"10.3390\/s16122009"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1111\/ajgw.12404","article-title":"On-the-Go Assessment of Vineyard Canopy Porosity, Bunch and Leaf Exposure by Image Analysis","volume":"25","author":"Diago","year":"2019","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Klodt, M., Herzog, K., T\u00f6pfer, R., and Cremers, D. (2015). Field Phenotyping of Grapevine Growth Using Dense Stereo Reconstruction. BMC Bioinform., 16.","DOI":"10.1186\/s12859-015-0560-x"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1007\/s11119-023-10006-y","article-title":"Using Deep Learning for Pruning Region Detection and Plant Organ Segmentation in Dormant Spur-Pruned Grapevines","volume":"24","author":"Guadagna","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fernandes, M., Scaldaferri, A., Fiameni, G., Teng, T., Gatti, M., Poni, S., Semini, C., Caldwell, D., and Chen, F. (2021, January 27\u201331). Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling Using Grapevine Segmentation. Proceedings of the 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Jiaxing, China.","DOI":"10.1109\/CYBER53097.2021.9588303"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1002\/rob.21680","article-title":"A Robot System for Pruning Grape Vines","volume":"34","author":"Botterill","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gao, M., and Lu, T. (2006, January 25\u201328). Image Processing and Analysis for Autonomous Grapevine Pruning. Proceedings of the 2006 International Conference on Mechatronics and Automation, Luoyang, China.","DOI":"10.1109\/ICMA.2006.257748"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1111\/ajgw.12118","article-title":"A New Method for Assessment of Bunch Compactness Using Automated Image Analysis","volume":"21","author":"Cubero","year":"2015","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1080\/01431161.2012.726753","article-title":"Object-Based Analysis of Grapevine Canopy Relationships with Winegrape Composition and Yield in Two Contrasting Vineyards Using Multitemporal High Spatial Resolution Optical Remote Sensing","volume":"34","author":"Hall","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Fern\u00e1ndez, M., Sanz-Ablanedo, E., Pereira-Obaya, D., and Rodr\u00edguez-P\u00e9rez, J.R. (2021). Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry. Agronomy, 11.","DOI":"10.3390\/agronomy11122489"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Siebers, M.H., Edwards, E.J., Jimenez-Berni, J.A., Thomas, M.R., Salim, M., and Walker, R.R. (2018). Fast Phenomics in Vineyards: Development of GRover, the Grapevine Rover, and LiDAR for Assessing Grapevine Traits in the Field. Sensors, 18.","DOI":"10.3390\/s18092924"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1007\/s11119-017-9519-4","article-title":"Evaluation of the Use of LIDAR Laser Scanner to Map Pruning Wood in Vineyards and Its Potential for Management Zones Delineation","volume":"19","author":"Tagarakis","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1111\/ajgw.12243","article-title":"Automatic Image-Based Determination of Pruning Mass as a Determinant for Yield Potential in Grapevine Management and Breeding","volume":"23","author":"Kicherer","year":"2017","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_48","first-page":"307","article-title":"Vineyard Pruning Weight Assessment by Machine Vision: Towards an on-the-Go Measurement System","volume":"53","author":"Diago","year":"2019","journal-title":"OENO One"},{"key":"ref_49","first-page":"1","article-title":"Initial Steps for High-Throughput Phenotyping in Vineyards","volume":"53","author":"Herzog","year":"2014","journal-title":"Vitis"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compag.2013.11.008","article-title":"Automated Image Analysis Framework for High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields","volume":"100","author":"Roscher","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1017\/S1431927618015428","article-title":"Weka Trainable Segmentation Plugin in ImageJ: A Semi-Automatic Tool Applied to Crystal Size Distributions of Microlites in Volcanic Rocks","volume":"24","author":"Lormand","year":"2018","journal-title":"Microsc. Microanal."},{"key":"ref_52","unstructured":"Witten, I.H., Frank, E., Trigg, L., Hall, M., and Holmes, G. (1999). Weka: Practical Machine Learning Tools and Techniques with Java Implementations, Computer Science Working Papers, Department of Computer Science, University of Waikato."},{"key":"ref_53","unstructured":"Broeke, J., P\u00e9rez, J.M.M., and Pascau, J. (2015). Image Processing with ImageJ, Packt Publishing Ltd."},{"key":"ref_54","first-page":"23","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"11","author":"Otsu","year":"1975","journal-title":"Automatica"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.3390\/s140203001","article-title":"Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping","volume":"14","author":"Paulus","year":"2014","journal-title":"Sensors"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.compag.2017.03.013","article-title":"A computer vision system for early stage grape yield estimation based on shoot detection","volume":"137","author":"Liu","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_57","first-page":"95","article-title":"Efficient identification, localization and quantification of grapevine inflorescences and flowers in unprepared field images using Fully Convolutional Networks","volume":"58","author":"Rudolph","year":"2019","journal-title":"Vitis. J. Grapevine Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1002\/rob.21553","article-title":"Automated Visual Yield Estimation in Vineyards","volume":"31","author":"Nuske","year":"2014","journal-title":"J. Field Robot."},{"key":"ref_59","first-page":"185","article-title":"Dynamics of starch reserves in some grapevine varieties (Vitis vinifera L.) during dormancy","volume":"76","author":"Cordea","year":"2019","journal-title":"Horticulture"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Victorino, G., Poblete-Echeverr\u00eda, C., and Lopes, C.M. (2022). A Multicultivar Approach for Grape Bunch Weight Estimation Using Image Analysis. Horticulturae, 8.","DOI":"10.3390\/horticulturae8030233"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.biosystemseng.2016.12.011","article-title":"A new methodology for estimating the grapevine-berry number per cluster using image analysis","volume":"156","author":"Aquino","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Jaramillo, J., Wilhelm, A., Napp, N., Heuvel, J.V., and Petersen, K. (2024, January 13\u201317). Inexpensive, Automated Pruning Weight Estimation in Vineyards. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10610164"}],"container-title":["Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0472\/15\/9\/966\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:24:11Z","timestamp":1760030651000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0472\/15\/9\/966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,29]]},"references-count":62,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["agriculture15090966"],"URL":"https:\/\/doi.org\/10.3390\/agriculture15090966","relation":{},"ISSN":["2077-0472"],"issn-type":[{"value":"2077-0472","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,29]]}}}