{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:39:44Z","timestamp":1778600384251,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"HORIZON 2020 agROBOfood project","award":["825395"],"award-info":[{"award-number":["825395"]}]},{"name":"HORIZON 2020 agROBOfood project","award":["739570"],"award-info":[{"award-number":["739570"]}]},{"name":"HORIZON 2020: ANTARES project","award":["825395"],"award-info":[{"award-number":["825395"]}]},{"name":"HORIZON 2020: ANTARES project","award":["739570"],"award-info":[{"award-number":["739570"]}]},{"name":"The Ministry of Education, Science and Technological Development of the Republic of Serbia","award":["825395"],"award-info":[{"award-number":["825395"]}]},{"name":"The Ministry of Education, Science and Technological Development of the Republic of Serbia","award":["739570"],"award-info":[{"award-number":["739570"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an autonomous robotic system, an unmanned ground vehicle (UGV), for in-field soil sampling and analysis of nitrates. Compared to standard methods of soil analysis it has several advantages: each sample is individually analyzed compared to average sample analysis in standard methods; each sample is georeferenced, providing a map for precision base fertilizing; the process is fully autonomous; samples are analyzed in real-time, approximately 30 min per sample; and lightweight for less soil compaction. The robotic system has several modules: commercial robotic platform, anchoring module, sampling module, sample preparation module, sample analysis module, and communication module. The system is augmented with an in-house developed cloud-based platform. This platform uses satellite images, and an artificial intelligence (AI) proprietary algorithm to divide the target field into representative zones for sampling, thus, reducing and optimizing the number and locations of the samples. Based on this, a task is created for the robot to automatically sample at those locations. The user is provided with an in-house developed smartphone app enabling overview and monitoring of the task, changing the positions, removing and adding of the sampling points. The results of the measurements are uploaded to the cloud for further analysis and the creation of prescription maps for variable rate base fertilization.<\/jats:p>","DOI":"10.3390\/s22114207","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T21:43:42Z","timestamp":1654119822000},"page":"4207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Agrobot Lala\u2014An Autonomous Robotic System for Real-Time, In-Field Soil Sampling, and Analysis of Nitrates"],"prefix":"10.3390","volume":"22","author":[{"given":"Goran","family":"Kiti\u0107","sequence":"first","affiliation":[{"name":"BioSense Institute\u2014Research Institute for Information Technologies in Biosystems, University of Novi Sad, Dr. Zorana \u0110in\u0111i\u0107a 1a, 21000 Novi Sad, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2279-4545","authenticated-orcid":false,"given":"Damir","family":"Krklje\u0161","sequence":"additional","affiliation":[{"name":"BioSense Institute\u2014Research Institute for Information Technologies in Biosystems, University of Novi Sad, Dr. Zorana \u0110in\u0111i\u0107a 1a, 21000 Novi Sad, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7993-6826","authenticated-orcid":false,"given":"Marko","family":"Pani\u0107","sequence":"additional","affiliation":[{"name":"BioSense Institute\u2014Research Institute for Information Technologies in Biosystems, University of Novi Sad, Dr. Zorana \u0110in\u0111i\u0107a 1a, 21000 Novi Sad, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Csaba","family":"Petes","sequence":"additional","affiliation":[{"name":"BioSense Institute\u2014Research Institute for Information Technologies in Biosystems, University of Novi Sad, Dr. Zorana \u0110in\u0111i\u0107a 1a, 21000 Novi Sad, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0117-8498","authenticated-orcid":false,"given":"Slobodan","family":"Birgermajer","sequence":"additional","affiliation":[{"name":"BioSense Institute\u2014Research Institute for Information Technologies in Biosystems, University of Novi Sad, Dr. Zorana \u0110in\u0111i\u0107a 1a, 21000 Novi Sad, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimir","family":"Crnojevi\u0107","sequence":"additional","affiliation":[{"name":"BioSense Institute\u2014Research Institute for Information Technologies in Biosystems, University of Novi Sad, Dr. Zorana \u0110in\u0111i\u0107a 1a, 21000 Novi Sad, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"ref_1","unstructured":"Bruinsma, J. (2003). Chapter 1 Introduction and overview. World Agriculture: Towards 2015\/2030, An FAO Perspective, Earthscan Publications Ltd.. [1st ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2006.10.001","article-title":"Elaboration of a Nitrogen Nutrition Indicator for Winter Wheat Based on Leaf Area Index and Chlorophyll Content for Making Nitrogen Recommendations","volume":"27","author":"Mary","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Carter, M.R., and Gregorich, E.G. (2006). Chapter 1 Soil Sampling design. Soil Sampling and Methods of Analysis, CRC Press, Taylor & Francis Group. [2nd ed.].","DOI":"10.1201\/9781420005271"},{"key":"ref_4","unstructured":"(2022, April 13). Four Soil Collection Methods That Actually Work. Available online: https:\/\/growers.ag\/blog\/4-soil-collection-methods-that-actually-work\/."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Dalmau, J., Berbel, J., and Ord\u00f3\u00f1ez-Fern\u00e1ndez, R. (2021). Nitrogen Fertilization. A Review of the Risks Associated with the Ineffi-ciency of Its Use and Policy Responses. Sustainability, 13.","DOI":"10.3390\/su13105625"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sikora, J., Niemiec, M., Szel\u0105g-Sikora, A., Gr\u00f3dek-Szostak, Z., Kubo\u0144, M., and Komorowska, M. (2020). The Impact of a Controlled-Release Fertilizer on Greenhouse Gas Emissions and the Efficiency of the Production of Chinese Cabbage. Energies, 13.","DOI":"10.3390\/en13082063"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e02R01","DOI":"10.5424\/sjar\/2017151-9573","article-title":"Task-based agricultural mobile robots in arable farming: A review","volume":"15","author":"Aravind","year":"2007","journal-title":"Span J. Agric. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"201","DOI":"10.17660\/ActaHortic.2009.824.23","article-title":"A Specification for an Autonomous Crop Production Mechanization System","volume":"824","author":"Blackmore","year":"2009","journal-title":"Acta Hortic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1071\/EA97158","article-title":"Soil Chemical Analytical Accuracy and Costs: Implications from Precision Agriculture","volume":"38","author":"Rossel","year":"1998","journal-title":"Aust. J. Exp. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1080\/00103629609369669","article-title":"Possibility of Different Soil Sampling Techniques with Automated Soil Sampler","volume":"27","author":"McGrath","year":"1996","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_11","unstructured":"(2022, April 13). Rogo Ag. Available online: https:\/\/rogoag.com\/."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1139\/juvs-2020-0003","article-title":"A UGV-Based Modular Robotic Manipulator for Soil Sampling and Terramechanics Investigations","volume":"8","author":"Olmedo","year":"2020","journal-title":"J. Unmanned Veh. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cao, P.M., Hall, E.L., and Zhang, E. (2003). Soil Sampling Sensor System on a Mobile Robot. SPIE Proc.","DOI":"10.1117\/12.516367"},{"key":"ref_14","first-page":"982","article-title":"Soil Sampling Automation Case-Study Using Unmanned Ground Vehicle","volume":"17","author":"Vaeljaots","year":"2018","journal-title":"Eng. Rural. Dev."},{"key":"ref_15","unstructured":"(2022, March 30). HUSKY Unmanned Ground Vehicle. Available online: https:\/\/clearpathrobotics.com\/husky-unmanned-ground-vehicle-robot\/#:~:text=Husky%20is%20a%20medium%20sized,UGV%20by%20our%20integration%20experts."},{"key":"ref_16","unstructured":"(2022, April 13). AgroSense Digital Platform. Available online: https:\/\/agrosens.rs\/#\/app-h\/about."},{"key":"ref_17","unstructured":"Sabbe, W.E., and Marx, D.B. (1987). Soil sampling: Spatial and temporal variability. Soil Testing: Sampling, Correlation, Calibration, and Interpretation, SSSA."},{"key":"ref_18","first-page":"6","article-title":"Grid soil sampling","volume":"78","author":"Wollenhaupt","year":"1994","journal-title":"Better Crops"},{"key":"ref_19","first-page":"513","article-title":"Use of site specific management zones to improve nitro-gen management for precision agriculture","volume":"57","author":"Khosla","year":"2002","journal-title":"J. Soil Water Conserv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ali, A., Rondelli, V., Martelli, R., Falsone, G., Lupia, F., and Barbanti, L. (2022). Management Zones Delineation through Clustering Techniques Based on Soils Traits, NDVI Data, and Multiple Year Crop Yields. Agriculture, 12.","DOI":"10.3390\/agriculture12020231"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"574","DOI":"10.2134\/agronj2003.5740","article-title":"Cluster Analysis of Spatiotemporal Corn Yield Patterns in an Iowa Field","volume":"95","author":"Jaynes","year":"2003","journal-title":"Agron. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"352","DOI":"10.2134\/agronj2003.3520","article-title":"Identifying Soil Properties That Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity","volume":"95","author":"Corwin","year":"2003","journal-title":"Agron. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/S0034-4257(03)00131-7","article-title":"Reflectance Measurement of Canopy Biomass and Nitrogen Status in Wheat Crops Using Normalized Difference Vegetation Indices and Partial Least Squares Regression","volume":"86","author":"Hansen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1007\/s11119-010-9183-4","article-title":"A Comparison of Different Algorithms for the Delineation of Management Zones","volume":"11","author":"Guastaferro","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1080\/01431168708954775","article-title":"Vegetation spatial variability and its effect on vegetation indices","volume":"8","author":"Ormsby","year":"1987","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1080\/01431169508954550","article-title":"Spatial variability of images and the monitoring of changes in the normalized difference vegetation index","volume":"16","author":"Townshend","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ali, A., Martelli, R., Lupia, F., and Barbanti, L. (2019). Assessing multiple years\u2019 spatial variability of crop yields using satellite vegetation indices. Remote Sens., 11.","DOI":"10.3390\/rs11202384"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.3390\/s8042136","article-title":"Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape","volume":"8","author":"Glenn","year":"2008","journal-title":"Sensors"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Marino, S., and Alvino, A. (2021). Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits. Remote Sens., 13.","DOI":"10.3390\/rs13040541"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"012028","DOI":"10.1088\/1742-6596\/1235\/1\/012028","article-title":"Mitigation & identification for local aridity, based of vegetation indices combined with spatial statistics & clustering k means. IOP Publishing","volume":"1235","author":"Praetyo","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"389","DOI":"10.31413\/nativa.v6i4.5405","article-title":"Segmentation of RGB images using different vegetation indices and thresholding methods","volume":"6","author":"Netto","year":"2018","journal-title":"Nativa"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2009.09.017","article-title":"Evaluation of Sensing Technologies for On-the-Go Detection of Macro-Nutrients in Cultivated Soils","volume":"70","author":"Sinfield","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","unstructured":"(2022, April 13). Vernier NO3-BTA. Available online: https:\/\/www.vernier.com\/manuals\/no3-bta\/."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bremner, J.M. (2016). Total Nitrogen. Agronomy Monographs, American Society of Agronomy.","DOI":"10.2134\/agronmonogr9.2.c32"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1100\/tsw.2001.308","article-title":"Factors Affecting Microbial Formation of Nitrate-Nitrogen in Soil and Their Effects on Fertilizer Nitrogen Use Efficiency","volume":"1","author":"Olness","year":"2001","journal-title":"Sci. World J."},{"key":"ref_36","unstructured":"(2022, April 14). Agrobot Lala. Available online: https:\/\/www.youtube.com\/watch?v=seU82D8w9RA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s10705-008-9180-4","article-title":"Soil Nitrate-N Levels Required for High Yield Maize Production in the North China Plain","volume":"82","author":"Cui","year":"2008","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_38","unstructured":"(2022, April 13). CleanGrow Multi-Ion Nutrient Analyzer Kit. Available online: https:\/\/www.ionselectiveelectrode.com\/products\/cleangrow-multi-ion-nutrient-analyzer-kit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/4207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:22:59Z","timestamp":1760138579000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/4207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,31]]},"references-count":38,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22114207"],"URL":"https:\/\/doi.org\/10.3390\/s22114207","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,31]]}}}