{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T01:34:26Z","timestamp":1772069666370,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T00:00:00Z","timestamp":1737763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Component 5\u2014Capitalization and Business Innovation, integrated in the Resilience Dimension of the Recovery and Resilience Plan within the scope of the Recovery and Resilience Mechanism (MRR) of the European Union (EU), framed in the Next Generation EU, for the period 2021\u20132026"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Agriculture"],"abstract":"<jats:p>Precision agriculture seeks to optimize crop yields while minimizing resource use. A key challenge is achieving uniform pesticide spraying to prevent crop damage and environmental contamination. Water-sensitive paper (WSP) is a common tool used for assessing spray quality, as it visually registers droplet impacts through color change. This work introduces a smartphone-based solution for capturing WSP images within vegetation, offering a tool for farmers to assess spray quality in real-world conditions. To achieve this, two approaches were explored: classical computer vision techniques and machine learning (ML) models (YOLOv8, Mask-RCNN, and Cellpose). Addressing the challenges of limited real-world data and the complexity of manual annotation, a programmatically generated synthetic dataset was employed to enable sim-to-real transfer learning. For the task of WSP segmentation within vegetation, YOLOv8 achieved an average Intersection over Union of 97.76%. In the droplet detection task, which involves identifying individual droplets on WSP, Cellpose achieved the highest precision of 96.18%, in the presence of overlapping droplets. While classical computer vision techniques provided a reliable baseline, they struggled with complex cases. Additionally, ML models, particularly Cellpose, demonstrated accurate droplet detection even without fine-tuning.<\/jats:p>","DOI":"10.3390\/agriculture15030261","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T06:39:51Z","timestamp":1737959991000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Spray Quality Assessment on Water-Sensitive Paper Comparing AI and Classical Computer Vision Methods"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2124-4351","authenticated-orcid":false,"given":"In\u00eas","family":"Sim\u00f5es","sequence":"first","affiliation":[{"name":"FEUP\u2014Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0317-4714","authenticated-orcid":false,"given":"Armando Jorge","family":"Sousa","sequence":"additional","affiliation":[{"name":"FEUP\u2014Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal"},{"name":"INESC TEC\u2014INESC Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4047-1395","authenticated-orcid":false,"given":"Andr\u00e9","family":"Baltazar","sequence":"additional","affiliation":[{"name":"INESC TEC\u2014INESC Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-6113","authenticated-orcid":false,"given":"Filipe","family":"Santos","sequence":"additional","affiliation":[{"name":"INESC TEC\u2014INESC Technology and Science, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1080\/2150704X.2020.1753338","article-title":"Remote Sensing Letters contribution to the success of the Sustainable Development Goals\u2014UN 2030 agenda","volume":"11","author":"Varotsos","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Privitera, S., Manetto, G., Pascuzzi, S., Pessina, D., and Cerruto, E. (2023). Drop size measurement techniques for agricultural sprays: A state-of-the-art review. Agronomy, 13.","DOI":"10.3390\/agronomy13030678"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Machado, B.B., Spadon, G., Arruda, M.S., Goncalves, W.N., Carvalho, A.C.P.L.F., and Rodrigues-Jr, J.F. (2018, January 9\u201313). A smartphone application to measure the quality of pest control spraying machines via image analysis. Proceedings of the 33rd Annual ACM Symposium on Applied Computing 2018, New York, NY, USA.","DOI":"10.1145\/3167132.3167237"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nansen, C., Villar, G.D., Recalde, A., Alvarado, E., and Chennapragada, K. (2021). Phone app to perform quality control of pesticide spray applications in field crops. Agriculture, 11.","DOI":"10.3390\/agriculture11100916"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"811","DOI":"10.13031\/2013.24519","article-title":"Image processing of artificial targets for automatic evaluation of spray quality","volume":"51","author":"Cunha","year":"2008","journal-title":"Trans. ASABE"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104861","DOI":"10.1016\/j.cropro.2019.104861","article-title":"A model to estimate the spray deposit by simulated water sensitive papers","volume":"124","author":"Cerruto","year":"2019","journal-title":"Crop Prot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/0169-8095(91)90024-Q","article-title":"Re-evaluation of surface ozone over Athens, Greece, for the period 1901\u20131940","volume":"26","author":"Varotsos","year":"1991","journal-title":"Atmos. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"961","DOI":"10.13031\/2013.20026","article-title":"Comparison of three imaging systems for water-sensitive papers","volume":"21","author":"Hoffmann","year":"2005","journal-title":"Appl. Eng. Agric."},{"key":"ref_9","unstructured":"Schick, R.J. (2008). Spray Technology Reference Guide: Understanding Drop Size, Spray Systems Co.. Spray Analysis and Research Services."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lefebvre, A.H., and McDonell, V.G. (2017). Atomization and Sprays, CRC Press.","DOI":"10.1201\/9781315120911"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.compag.2011.01.003","article-title":"A portable scanning system for evaluation of spray deposit distribution","volume":"76","author":"Zhu","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1007\/s13593-015-0309-y","article-title":"Optimizing pesticide spray coverage using a novel web and smartphone tool, SnapCard","volume":"35","author":"Nansen","year":"2015","journal-title":"Agron. Sustain. Dev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105104","DOI":"10.1016\/j.compag.2019.105104","article-title":"Development and assessment of a novel imaging software for optimizing the spray parameters on water-sensitive papers","volume":"168","author":"Bolat","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106492","DOI":"10.1016\/j.cropro.2023.106492","article-title":"A novel methodology for water-sensitive papers analysis focusing on the segmentation of overlapping droplets to better characterize deposition pattern","volume":"176","author":"Xun","year":"2024","journal-title":"Crop Prot."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, T., Meng, Y., Su, J., and Liu, C. (2022, January 1\u20133). Deep CNN based droplet deposition segmentation for spray distribution assessment. Proceedings of the 2022 27th International Conference on Automation and Computing (ICAC), Bristol, UK.","DOI":"10.1109\/ICAC55051.2022.9911061"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107038","DOI":"10.1016\/j.compag.2022.107038","article-title":"Droplet deposition characteristics detection method based on deep learning","volume":"198","author":"Yang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","first-page":"65","article-title":"Evaluation of UAV spraying quality based on 1D-CNN model and wireless multi-sensors system","volume":"11","author":"Hao","year":"2022","journal-title":"Inform. Process. Agric."},{"key":"ref_18","unstructured":"D\u00e9chelette, A., Babinsky, E., and Sojka, P. (2011). Handbook of Atomization and Sprays, Springer."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1021\/ie50498a023","article-title":"Droplet size distribution in sprays","volume":"43","author":"Mugele","year":"1951","journal-title":"Ind. Eng. Chem."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104949","DOI":"10.1016\/j.cropro.2019.104949","article-title":"Binarizing water sensitive papers\u2014How to assess the coverage area properly?","volume":"127","year":"2020","journal-title":"Crop Prot."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wen, T., Tong, B., Liu, Y., Pan, T., Du, Y., Chen, Y., and Zhang, S. (2022). Review of research on the instance segmentation of cell images. Comput. Methods Programs Biomed., 227.","DOI":"10.1016\/j.cmpb.2022.107211"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Elsalamony, H.A. (2015). Detecting distorted and benign blood cells using the Hough transform based on neural networks and decision trees. Emerging Trends in Image Processing, Computer Vision and Pattern Recognition, Morgan Kaufmann.","DOI":"10.1016\/B978-0-12-802045-6.00030-2"},{"key":"ref_23","first-page":"300","article-title":"Spray Quality Assessment on Water-Sensitive Paper Comparing AI and Classical Computer Vision Methods","volume":"Volume 2","author":"Baltazar","year":"2024","journal-title":"Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO)"}],"container-title":["Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0472\/15\/3\/261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:36:05Z","timestamp":1759919765000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0472\/15\/3\/261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,25]]},"references-count":23,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["agriculture15030261"],"URL":"https:\/\/doi.org\/10.3390\/agriculture15030261","relation":{},"ISSN":["2077-0472"],"issn-type":[{"value":"2077-0472","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,25]]}}}