{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T11:57:20Z","timestamp":1776686240818,"version":"3.51.2"},"reference-count":103,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006752","name":"Universidade do Porto","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006752","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Robot Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This paper covers the state-of-the-art perception and control technologies in precision spraying and mowing in permanent crops. The search was performed in six different databases, resulting in 1849 publications, from which only 94 were considered for inclusion in this review. The analysis highlighted the importance of canopy characteristics in precision spraying, focusing on parameters like height, width, leaf area, and volume, primarily using LiDAR sensors. Vision sensors also complemented LiDAR-based approaches, with diverse applications such as fruit detection and disease diagnosis. Despite valuable knowledge from studies on spray coverage assessment and real-time smartphone analysis, challenges persist, including dynamic environmental factors and the different collector materials used. Moreover, the review considers the cost of Variable Rate Technology (VRT) solutions in agriculture, enhancing their impact on accessibility, adoption, and sustainability. While conventional herbicide-based weed management prevails, interest in alternative techniques like mechanical mowing and organic mulches is growing, promising improved soil health and reduced environmental impact, particularly in permanent crops. To address these challenges, agricultural robotics play a crucial role in automating precision spraying and mowing, optimizing resource usage, and increasing operational precision. This systematic review highlights the state of precision agriculture in permanent crops and emphasizes the need for continued research and development to improve the sustainability and efficiency of precision spraying and mowing systems in orchards, vineyards, and other woody crop environments.<\/jats:p>","DOI":"10.1007\/s10846-026-02396-8","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T11:08:27Z","timestamp":1776683307000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Perception and Control for Precision Spraying and Mowing in Woody Crops \u2013 Systematic Review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4047-1395","authenticated-orcid":false,"given":"Andr\u00e9","family":"Rodrigues\u00a0Baltazar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-6113","authenticated-orcid":false,"given":"Filipe","family":"Neves\u00a0dos\u00a0Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8573-3147","authenticated-orcid":false,"given":"Ant\u00f3nio Paulo","family":"Moreira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8406-0064","authenticated-orcid":false,"given":"Jos\u00e9","family":"Boaventura\u00a0Cunha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"2396_CR1","unstructured":"Agriculture, N.A.S.S.: Agricultural chemical use survey: Fruit crops 2015. Technical report, U.S. Department of Agriculture. (2015). https:\/\/www.nass.usda.gov\/Surveys\/Guide_to_NASS_Surveys\/Chemical_Use\/2015_Fruits\/ChemUseHighlights_Fruit_2015.pdf. Accessed 2025-03-12"},{"issue":"3","key":"2396_CR2","doi-asserted-by":"publisher","first-page":"674","DOI":"10.3390\/agriengineering4030043","volume":"4","author":"S Fountas","year":"2022","unstructured":"Fountas, S., Malounas, I., Athanasakos, L., Avgoustakis, I., Espejo-Garcia, B.: Ai-assisted vision for agricultural robots. AgriEngineering 4(3), 674\u2013694 (2022). https:\/\/doi.org\/10.3390\/agriengineering4030043","journal-title":"Ai-assisted vision for agricultural robots. AgriEngineering"},{"key":"2396_CR3","doi-asserted-by":"publisher","unstructured":"Wandkar, S.V., Bhatt, Y.C., Jain, H.K., Nalawade, S.M., Pawar, S.G.: Real-time variable rate spraying in orchards and vineyards: A review. Journal of The Institution of Engineers (India): Series A 99(2), 385\u2013390 (2018). https:\/\/doi.org\/10.1007\/s40030-018-0289-4","DOI":"10.1007\/s40030-018-0289-4"},{"key":"2396_CR4","doi-asserted-by":"publisher","first-page":"188","DOI":"10.31803\/tg-20180213125928","volume":"12","author":"B \u017deljko","year":"2018","unstructured":"\u017deljko, B., Petrovic, D.: Different sensor systems for the application of variable rate technology in permanent crops. Tehni\u010dki glasnik 12, 188\u2013195 (2018). https:\/\/doi.org\/10.31803\/tg-20180213125928","journal-title":"Tehni\u010dki glasnik"},{"key":"2396_CR5","unstructured":"G., M., Banerjee, M., Malik, G., N., S.: Robotics in weed management: A review. Ama, Agricultural Mechanization in Asia, Africa & Latin America 53, 7013\u20137030 (2022)"},{"key":"2396_CR6","doi-asserted-by":"publisher","unstructured":"Veeragandham, S., Santhi, H.: A detailed review on challenges and imperatives of various cnn algorithms in weed detection. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 1068\u20131073 (2021). https:\/\/doi.org\/10.1109\/ICAIS50930.2021.9395986","DOI":"10.1109\/ICAIS50930.2021.9395986"},{"key":"2396_CR7","doi-asserted-by":"publisher","unstructured":"Wohlin, C., Runeson, P., H\u00f6st, M., Ohlsson, M., Regnell, B., Wessl\u00e9n, A.: Experimentation in Software Engineering. Springer, Germany (2012). https:\/\/doi.org\/10.1007\/978-3-642-29044-2","DOI":"10.1007\/978-3-642-29044-2"},{"key":"2396_CR8","doi-asserted-by":"publisher","unstructured":"Page, M.J., Moher, D., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hr\u00f3bjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuinness, L.A., Stewart, L.A., Thomas, J., Tricco, A.C., Welch, V.A., Whiting, P., McKenzie, J.E.: Prisma 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 372 (2021). https:\/\/doi.org\/10.1136\/bmj.n160, https:\/\/www.bmj.com\/content\/372\/bmj.n160.full.pdf","DOI":"10.1136\/bmj.n160"},{"key":"2396_CR9","doi-asserted-by":"publisher","unstructured":"Nansen, C., Villar, G.D., Recalde, A., Alvarado, E., Chennapragada, K.: Phone app to perform quality control of pesticide spray applications in field crops. Agriculture 11(10) (2021). https:\/\/doi.org\/10.3390\/agriculture11100916","DOI":"10.3390\/agriculture11100916"},{"key":"2396_CR10","volume-title":"Measuring Tree Canopy Density Using A Lidar-Guided System for Precision Spraying","author":"MS Mahmud","year":"2020","unstructured":"Mahmud, M.S., He, L.: Measuring Tree Canopy Density Using A Lidar-Guided System for Precision Spraying. ASABE, St. Joseph, MI (2020)"},{"key":"2396_CR11","doi-asserted-by":"publisher","unstructured":"Liu, H., Gao, B., Shen, Y., Hussain, F., Addis, D., Pan, C.: Comparison of sick and hokuyo utm-30lx laser sensors in canopy detection for variable-rate sprayer. Information Processing in Agriculture 5 (2018). https:\/\/doi.org\/10.1016\/j.inpa.2018.06.001","DOI":"10.1016\/j.inpa.2018.06.001"},{"key":"2396_CR12","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.biosystemseng.2020.10.016","volume":"200","author":"A Chera\u00efet","year":"2020","unstructured":"Chera\u00efet, A., Naud, O., Carra, M., Codis, S., Lebeau, F., Taylor, J.: An algorithm to automate the filtering and classifying of 2d lidar data for site-specific estimations of canopy height and width in vineyards. Biosys. Eng. 200, 450\u2013465 (2020). https:\/\/doi.org\/10.1016\/j.biosystemseng.2020.10.016","journal-title":"Biosys. Eng."},{"key":"2396_CR13","doi-asserted-by":"publisher","unstructured":"Liu, L., Liu, Y., He, X., Liu, W.: Precision variable-rate spraying robot by using single 3d lidar in orchards. Agronomy 12(10) (2022). https:\/\/doi.org\/10.3390\/agronomy12102509","DOI":"10.3390\/agronomy12102509"},{"key":"2396_CR14","doi-asserted-by":"crossref","first-page":"101","DOI":"10.25165\/j.ijabe.20181101.3183","volume":"11","author":"L Li","year":"2018","unstructured":"Li, L., He, X., Song, J., Liu, Y., Zeng, A., Yang, L., Liu, C., Liu, Z.: Design and experiment of variable rate orchard sprayer based on laser scanning sensor. Int. J. Agricul. Biol. Eng. 11, 101\u2013108 (2018)","journal-title":"Int. J. Agricul. Biol. Eng."},{"key":"2396_CR15","doi-asserted-by":"publisher","unstructured":"Gu, C., Zhai, C., Wang, X., Wang, S.: Cmpc: An innovative lidar-based method to estimate tree canopy meshing-profile volumes for orchard target-oriented spray. Sensors 21(12) (2021). https:\/\/doi.org\/10.3390\/s21124252","DOI":"10.3390\/s21124252"},{"key":"2396_CR16","doi-asserted-by":"publisher","unstructured":"Liu, X., Wang, Y., Kang, F., Yue, Y., Zheng, Y.: Canopy parameter estimation of citrus grandis var. longanyou based on lidar 3d point clouds. Remote Sensing 13(9) (2021). https:\/\/doi.org\/10.3390\/rs13091859","DOI":"10.3390\/rs13091859"},{"key":"2396_CR17","doi-asserted-by":"publisher","unstructured":"Gu, C., Zhao, C., Zou, W., Yang, S., Dou, H., Zhai, C.: Innovative leaf area detection models for orchard tree thick canopy based on lidar point cloud data. Agriculture 12(8) (2022). https:\/\/doi.org\/10.3390\/agriculture12081241","DOI":"10.3390\/agriculture12081241"},{"key":"2396_CR18","doi-asserted-by":"publisher","first-page":"106053","DOI":"10.1016\/j.compag.2021.106053","volume":"182","author":"M Sultan Mahmud","year":"2021","unstructured":"Sultan Mahmud, M., Zahid, A., He, L., Choi, D., Krawczyk, G., Zhu, H., Heinemann, P.: Development of a lidar-guided section-based tree canopy density measurement system for precision spray applications. Comput. Electron. Agric. 182, 106053 (2021). https:\/\/doi.org\/10.1016\/j.compag.2021.106053","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR19","doi-asserted-by":"publisher","first-page":"106565","DOI":"10.1016\/j.compag.2021.106565","volume":"191","author":"M Sultan Mahmud","year":"2021","unstructured":"Sultan Mahmud, M., Zahid, A., He, L., Choi, D., Krawczyk, G., Zhu, H.: Lidar-sensed tree canopy correction in uneven terrain conditions using a sensor fusion approach for precision sprayers. Comput. Electron. Agric. 191, 106565 (2021). https:\/\/doi.org\/10.1016\/j.compag.2021.106565","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR20","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.biosystemseng.2020.05.015","volume":"196","author":"L Zeng","year":"2020","unstructured":"Zeng, L., Feng, J., He, L.: Semantic segmentation of sparse 3d point cloud based on geometrical features for trellis-structured apple orchard. Biosys. Eng. 196, 46\u201355 (2020). https:\/\/doi.org\/10.1016\/j.biosystemseng.2020.05.015","journal-title":"Biosys. Eng."},{"key":"2396_CR21","doi-asserted-by":"publisher","unstructured":"Mahmud, M.S., Zahid, A., He, L.: Development of an automatic airflow control system for precision sprayers based on tree canopy density. (2021). https:\/\/doi.org\/10.13031\/aim.202100132","DOI":"10.13031\/aim.202100132"},{"key":"2396_CR22","doi-asserted-by":"publisher","first-page":"389","DOI":"10.15159\/ar.21.159","volume":"20","author":"A Pagliai","year":"2022","unstructured":"Pagliai, A., Sarri, D., Lisci, R., Lombardo, S., Vieri, M., Perna, C., Cencini, G., De Pascale, V., Ferraz, G.A.E.S.: Development of an algorithm for assessing canopy volumes with terrestrial lidar to implement precision spraying in vineyards. Agron. Res. 20, 389\u2013403 (2022). https:\/\/doi.org\/10.15159\/ar.21.159","journal-title":"Agron. Res."},{"key":"2396_CR23","doi-asserted-by":"publisher","unstructured":"Siefen, N., Mccormick, R., Vogel, A.M., Biegert, K.: Effects of laser scanner quality and tractor speed to characterise apple tree canopies, 100173 (2023). https:\/\/doi.org\/10.1016\/j.atech.2023.100173","DOI":"10.1016\/j.atech.2023.100173"},{"key":"2396_CR24","doi-asserted-by":"publisher","first-page":"43583","DOI":"10.1109\/ACCESS.2023.3271973","volume":"11","author":"AR Baltazar","year":"2023","unstructured":"Baltazar, A.R., Santos, F.N.D., De Sousa, M.L., Moreira, A.P., Cunha, J.B.: 2d lidar-based system for canopy sensing in smart spraying applications. IEEE Access 11, 43583\u201343591 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3271973","journal-title":"IEEE Access"},{"key":"2396_CR25","doi-asserted-by":"publisher","unstructured":"Wang, M., Dou, H., Sun, H., Zhai, C., Zhang, Y., Yuan, F.: Calculation method of canopy dynamic meshing division volumes for precision pesticide application in orchards based on lidar. Agronomy 13(4) (2023). https:\/\/doi.org\/10.3390\/agronomy13041077","DOI":"10.3390\/agronomy13041077"},{"key":"2396_CR26","doi-asserted-by":"publisher","unstructured":"Guo, N., Xu, N., Kang, J., Zhang, G., Meng, Q., Niu, M., Wu, W., Zhang, X.: A study on canopy volume measurement model for fruit tree application based on lidar point cloud. Agriculture 15(2) (2025). https:\/\/doi.org\/10.3390\/agriculture15020130","DOI":"10.3390\/agriculture15020130"},{"key":"2396_CR27","doi-asserted-by":"publisher","first-page":"109056","DOI":"10.1016\/j.compag.2024.109056","volume":"222","author":"H Liu","year":"2024","unstructured":"Liu, H., Du, Z., Shen, Y., Du, W., Zhang, X.: Development and evaluation of an intelligent multivariable spraying robot for orchards and nurseries. Comput. Electron. Agric. 222, 109056 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.109056","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR28","doi-asserted-by":"publisher","unstructured":"Karim, M.R., Ahmed, S., Reza, M.N., Lee, K.-H., Sung, J., Chung, S.-O.: Geometric feature characterization of apple trees from 3d lidar point cloud data. Journal of Imaging 11(1) (2025). https:\/\/doi.org\/10.3390\/jimaging11010005","DOI":"10.3390\/jimaging11010005"},{"key":"2396_CR29","doi-asserted-by":"publisher","first-page":"105412","DOI":"10.1016\/j.compag.2020.105412","volume":"173","author":"G Gao","year":"2020","unstructured":"Gao, G., Xiao, K., Jia, Y.: A spraying path planning algorithm based on colour-depth fusion segmentation in peach orchards. Comput. Electron. Agric. 173, 105412 (2020). https:\/\/doi.org\/10.1016\/j.compag.2020.105412","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR30","doi-asserted-by":"publisher","unstructured":"Mengoli, D., Bortolotti, G., Bartolomei, M., Allegro, G., Filippetti, I., Manfrini, L.: A lightweight and affordable method for canopy porosity estimation for precision spraying. In: 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 331\u2013336 (2023). https:\/\/doi.org\/10.1109\/MetroAgriFor58484.2023.10424337","DOI":"10.1109\/MetroAgriFor58484.2023.10424337"},{"key":"2396_CR31","doi-asserted-by":"publisher","unstructured":"Sun, D., Quan, Z., Wu, P., Liu, W., Xue, X., Song, S., Xie, J., Jiang, S.: Design and testing of a fruit tree variable spray system based on exg-aabb. Agronomy 14(10) (2024). https:\/\/doi.org\/10.3390\/agronomy14102199","DOI":"10.3390\/agronomy14102199"},{"key":"2396_CR32","doi-asserted-by":"publisher","unstructured":"Baltazar, A.R., Santos, F.N.d., Moreira, A.P., Valente, A., Cunha, J.B.: Smarter robotic sprayer system for precision agriculture. Electronics 10(17) (2021). https:\/\/doi.org\/10.3390\/electronics10172061","DOI":"10.3390\/electronics10172061"},{"key":"2396_CR33","doi-asserted-by":"publisher","first-page":"109168","DOI":"10.1016\/j.compag.2024.109168","volume":"224","author":"Z Khan","year":"2024","unstructured":"Khan, Z., Liu, H., Shen, Y., Zeng, X.: Deep learning improved yolov8 algorithm: Real-time precise instance segmentation of crown region orchard canopies in natural environment. Comput. Electron. Agric. 224, 109168 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.109168","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR34","doi-asserted-by":"publisher","unstructured":"Zhang, W., Chen, X., Qi, J., Yang, S.: Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning. Frontiers in Plant Science 13 (2022). https:\/\/doi.org\/10.3389\/fpls.2022.1041791","DOI":"10.3389\/fpls.2022.1041791"},{"key":"2396_CR35","doi-asserted-by":"publisher","unstructured":"Qin, Z., Wang, W., Dammer, K.-H., Guo, L., Cao, Z.: Ag-yolo: A real-time low-cost detector for precise spraying with case study of palms. Front. Plant Sci. 12 (2021). https:\/\/doi.org\/10.3389\/fpls.2021.753603","DOI":"10.3389\/fpls.2021.753603"},{"key":"2396_CR36","doi-asserted-by":"publisher","first-page":"146111","DOI":"10.1016\/j.scitotenv.2021.146111","volume":"778","author":"A Rodr\u00edguez-Lizana","year":"2021","unstructured":"Rodr\u00edguez-Lizana, A., Pereira, M.J., Ribeiro, M.C., Soares, A., Azevedo, L., Miranda-Fuentes, A., Llorens, J.: Spatially variable pesticide application in olive groves: Evaluation of potential pesticide-savings through stochastic spatial simulation algorithms. Sci. Total Environ. 778, 146111 (2021). https:\/\/doi.org\/10.1016\/j.scitotenv.2021.146111","journal-title":"Sci. Total Environ."},{"key":"2396_CR37","doi-asserted-by":"publisher","first-page":"100153","DOI":"10.1016\/j.atech.2022.100153","volume":"4","author":"MS Mahmud","year":"2023","unstructured":"Mahmud, M.S., He, L., Heinemann, P., Choi, D., Zhu, H.: Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications. Smart Agricultural Technology 4, 100153 (2023). https:\/\/doi.org\/10.1016\/j.atech.2022.100153","journal-title":"Smart Agricultural Technology"},{"key":"2396_CR38","doi-asserted-by":"publisher","first-page":"108197","DOI":"10.1016\/j.compag.2023.108197","volume":"213","author":"R Zhang","year":"2023","unstructured":"Zhang, R., Lian, S., Li, L., Zhang, L., Zhang, C., Chen, L.: Design and experiment of a binocular vision-based canopy volume extraction system for precision pesticide application by uavs. Comput. Electron. Agric. 213, 108197 (2023). https:\/\/doi.org\/10.1016\/j.compag.2023.108197","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR39","doi-asserted-by":"publisher","first-page":"109425","DOI":"10.1016\/j.compag.2024.109425","volume":"226","author":"P Wei","year":"2024","unstructured":"Wei, P., Yan, X., Yan, W., Sun, L., Xu, J., Yuan, H.: Precise extraction of targeted apple tree canopy with yolo-fi model for advanced uav spraying plans. Comput. Electron. Agric. 226, 109425 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.109425","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR40","doi-asserted-by":"publisher","first-page":"108538","DOI":"10.1016\/j.compag.2023.108538","volume":"217","author":"Z Li","year":"2024","unstructured":"Li, Z., Deng, X., Lan, Y., Liu, C., Qing, J.: Fruit tree canopy segmentation from uav orthophoto maps based on a lightweight improved u-net. Comput. Electron. Agric. 217, 108538 (2024). https:\/\/doi.org\/10.1016\/j.compag.2023.108538","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR41","doi-asserted-by":"publisher","unstructured":"Cunha, J., Gaspar, P., Assun\u00e7\u00e3o, E., Mesquita, R.: Prediction of the Vigor and Health of Peach Tree Orchard, pp. 541\u2013551 (2021). https:\/\/doi.org\/10.1007\/978-3-030-86970-0_38","DOI":"10.1007\/978-3-030-86970-0_38"},{"key":"2396_CR42","doi-asserted-by":"publisher","unstructured":"S.Patil, S., Patil, Y.M., Patil, S.B.: Detection and estimation of tree canopy using deep learning and sensor fusion. In: 2023 International Conference for Advancement in Technology (ICONAT), pp. 1\u20135 (2023). https:\/\/doi.org\/10.1109\/ICONAT57137.2023.10080785","DOI":"10.1109\/ICONAT57137.2023.10080785"},{"key":"2396_CR43","doi-asserted-by":"publisher","first-page":"109062","DOI":"10.1016\/j.compag.2024.109062","volume":"222","author":"W Li","year":"2024","unstructured":"Li, W., Yang, S., Zhao, H., Jiang, S., Zheng, Y., Liu, X., Tan, Y.: Deep learning method for leaf-density estimation based on wind-excited audio of fruit-tree canopies. Comput. Electron. Agric. 222, 109062 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.109062","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR44","doi-asserted-by":"publisher","unstructured":"Ciarfuglia, T.A., Marian\u00a0Motoi, I., Saraceni, L., Nardi, D.: Pseudo-label generation for agricultural robotics applications. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1685\u20131693 (2022). https:\/\/doi.org\/10.1109\/CVPRW56347.2022.00175","DOI":"10.1109\/CVPRW56347.2022.00175"},{"issue":"6","key":"2396_CR45","doi-asserted-by":"publisher","first-page":"8139","DOI":"10.1007\/s11042-022-11905-4","volume":"81","author":"K Zou","year":"2022","unstructured":"Zou, K., Ge, L., Zhou, H., Zhang, C., Li, W.: An apple image segmentation method based on a color index obtained by a genetic algorithm. Multimedia Tools and Applications 81(6), 8139\u20138153 (2022). https:\/\/doi.org\/10.1007\/s11042-022-11905-4","journal-title":"Multimedia Tools and Applications"},{"key":"2396_CR46","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.compag.2018.05.019","volume":"151","author":"R P\u00e9rez-Zavala","year":"2018","unstructured":"P\u00e9rez-Zavala, R., Torres-Torriti, M., Cheein, F.A., Troni, G.: A pattern recognition strategy for visual grape bunch detection in vineyards. Comput. Electron. Agric. 151, 136\u2013149 (2018). https:\/\/doi.org\/10.1016\/j.compag.2018.05.019","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR47","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/978-3-031-59167-9_27","volume-title":"Robot 2023: Sixth Iberian Robotics Conference","author":"T Deguchi","year":"2024","unstructured":"Deguchi, T., Baltazar, A.R., Santos, F.N., Mendon\u00e7a, H.: Vision-based smart sprayer for precision farming. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds.) Robot 2023: Sixth Iberian Robotics Conference, pp. 324\u2013335. Springer, Cham (2024)"},{"key":"2396_CR48","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.biosystemseng.2020.08.015","volume":"198","author":"H Gan","year":"2020","unstructured":"Gan, H., Lee, W.S., Alchanatis, V., Abd-Elrahman, A.: Active thermal imaging for immature citrus fruit detection. Biosys. Eng. 198, 291\u2013303 (2020). https:\/\/doi.org\/10.1016\/j.biosystemseng.2020.08.015","journal-title":"Biosys. Eng."},{"key":"2396_CR49","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/978-981-16-0739-4_76","volume-title":"Information and Communication Technology for Competitive Strategies (ICTCS 2020)","author":"PD Kalwad","year":"2022","unstructured":"Kalwad, P.D., Kanakaraddi, S.G., Preeti, T., Ichalakaranji, S., Salimath, S., Nayak, S.: Apple leaf disease detection and analysis using deep learning technique. In: Joshi, A., Mahmud, M., Ragel, R.G., Thakur, N.V. (eds.) Information and Communication Technology for Competitive Strategies (ICTCS 2020), pp. 803\u2013814. Springer, Singapore (2022)"},{"key":"2396_CR50","doi-asserted-by":"publisher","unstructured":"Ukaegbu, U., Tartibu, L., Laseinde, T., Okwu, M., Olayode, I.: A deep learning algorithm for detection of potassium deficiency in a red grapevine and spraying actuation using a raspberry pi3. In: 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), pp. 1\u20136 (2020). https:\/\/doi.org\/10.1109\/icABCD49160.2020.9183810","DOI":"10.1109\/icABCD49160.2020.9183810"},{"key":"2396_CR51","doi-asserted-by":"publisher","unstructured":"Fowad, D., Sharjeel, M., Ansere, J.A., Kamal, M.: Computer vision enabled plant\u2019s health estimation in precision farming. In: 2022 17th International Conference on Emerging Technologies (ICET), pp. 119\u2013124 (2022). https:\/\/doi.org\/10.1109\/ICET56601.2022.10004676","DOI":"10.1109\/ICET56601.2022.10004676"},{"key":"2396_CR52","doi-asserted-by":"publisher","unstructured":"H, A., H, Y.B., N, Y.: Leaf disease detection and prevention using deep learning. In: 2022 International Conference on Artificial Intelligence and Data Engineering (AIDE), pp. 223\u2013229 (2022). https:\/\/doi.org\/10.1109\/AIDE57180.2022.10060181","DOI":"10.1109\/AIDE57180.2022.10060181"},{"key":"2396_CR53","doi-asserted-by":"publisher","first-page":"108132","DOI":"10.1016\/j.compag.2023.108132","volume":"212","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Zhou, G., Chen, A., He, M., Li, J., Hu, Y.: A precise apple leaf diseases detection using bctnet under unconstrained environments. Comput. Electron. Agric. 212, 108132 (2023). https:\/\/doi.org\/10.1016\/j.compag.2023.108132","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR54","doi-asserted-by":"publisher","first-page":"109969","DOI":"10.1016\/j.compag.2025.109969","volume":"231","author":"Z Khan","year":"2025","unstructured":"Khan, Z., Liu, H., Shen, Y., Yang, Z., Zhang, L., Yang, F.: Optimizing precision agriculture: A real-time detection approach for grape vineyard unhealthy leaves using deep learning improved yolov7 with feature extraction capabilities. Comput. Electron. Agric. 231, 109969 (2025). https:\/\/doi.org\/10.1016\/j.compag.2025.109969","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR55","doi-asserted-by":"publisher","unstructured":"Araaf, R.T., Minn, A., Ahamed, T.: Coffee leaf rust disease detection and implementation of an edge device for pruning infected leaves via deep learning algorithms. Sensors 24(24) (2024). https:\/\/doi.org\/10.3390\/s24248018","DOI":"10.3390\/s24248018"},{"key":"2396_CR56","doi-asserted-by":"publisher","unstructured":"Ang, G., Han, R., Yuepeng, S., Longlong, R., Yue, Z., Xiang, H.: Construction and verification of machine vision algorithm model based on apple leaf disease images. Front. Plant Sci. 14 - 2023 (2023). https:\/\/doi.org\/10.3389\/fpls.2023.1246065","DOI":"10.3389\/fpls.2023.1246065"},{"key":"2396_CR57","doi-asserted-by":"publisher","unstructured":"Longhi, V., Martino, A., Lingua, A.M., Maschio, P.F., Belcore, E.: Monitoring the spread of a pathogenic insect on vineyards using uas. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024, 443\u2013450 (2024). https:\/\/doi.org\/10.5194\/isprs-archives-XLVIII-1-2024-443-2024","DOI":"10.5194\/isprs-archives-XLVIII-1-2024-443-2024"},{"key":"2396_CR58","doi-asserted-by":"publisher","DOI":"10.1002\/rob.22217","author":"M Lippi","year":"2023","unstructured":"Lippi, M., Santilli, M., Carpio, R., Maiolini, J., Garone, E., Cristofori, V., Gasparri, A.: An autonomous spraying robot architecture for sucker management in large-scale hazelnut orchards. J. Field Robot. (2023). https:\/\/doi.org\/10.1002\/rob.22217","journal-title":"J. Field Robot."},{"key":"2396_CR59","doi-asserted-by":"publisher","unstructured":"Gu, C., Zou, W., Wang, X., Chen, L., Zhai, C.: Wind loss model for the thick canopies of orchard trees based on accurate variable spraying. Front. Plant Sci. 13 (2022). https:\/\/doi.org\/10.3389\/fpls.2022.1010540","DOI":"10.3389\/fpls.2022.1010540"},{"key":"2396_CR60","doi-asserted-by":"publisher","unstructured":"Aliabad, F.A., Shojaei, S., Mortaz, M., Ferreira, C.S.S., Kalantari, Z.: Use of landsat 8 and uav images to assess changes in temperature and evapotranspiration by economic trees following foliar spraying with light-reflecting compounds. Remote Sensing 14(23) (2022). https:\/\/doi.org\/10.3390\/rs14236153","DOI":"10.3390\/rs14236153"},{"issue":"4","key":"2396_CR61","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1109\/TIT.1983.1056714","volume":"29","author":"H Edelsbrunner","year":"1983","unstructured":"Edelsbrunner, H., Kirkpatrick, D., Seidel, R.: On the shape of a set of points in the plane. IEEE Trans. Inf. Theory 29(4), 551\u2013559 (1983). https:\/\/doi.org\/10.1109\/TIT.1983.1056714","journal-title":"IEEE Trans. Inf. Theory"},{"key":"2396_CR62","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.cropro.2018.04.015","volume":"111","author":"G Gao","year":"2018","unstructured":"Gao, G., Xiao, K., Ma, Y.: A leaf-wall-to-spray-device distance and leaf-wall-density-based automatic route-planning spray algorithm for vineyards. Crop Prot. 111, 33\u201341 (2018). https:\/\/doi.org\/10.1016\/j.cropro.2018.04.015","journal-title":"Crop Prot."},{"key":"2396_CR63","doi-asserted-by":"publisher","unstructured":"Cheng, Z., Qi, L., Cheng, Y.: Cherry tree crown extraction from natural orchard images with complex backgrounds. Agriculture 11(5) (2021). https:\/\/doi.org\/10.3390\/agriculture11050431","DOI":"10.3390\/agriculture11050431"},{"key":"2396_CR64","doi-asserted-by":"publisher","unstructured":"Mahmud, M.S., Zahid, A., He, L., Martin, P.: Opportunities and possibilities of developing an advanced precision spraying system for tree fruits. Sensors 21(9) (2021). https:\/\/doi.org\/10.3390\/s21093262","DOI":"10.3390\/s21093262"},{"key":"2396_CR65","doi-asserted-by":"publisher","first-page":"100114","DOI":"10.1016\/j.atech.2022.100114","volume":"3","author":"T Thorat","year":"2023","unstructured":"Thorat, T., Patle, B.K., Kashyap, S.K.: Intelligent insecticide and fertilizer recommendation system based on tpf-cnn for smart farming. Smart Agricultural Technology 3, 100114 (2023). https:\/\/doi.org\/10.1016\/j.atech.2022.100114","journal-title":"Smart Agricultural Technology"},{"key":"2396_CR66","doi-asserted-by":"publisher","first-page":"18","DOI":"10.25165\/j.ijabe.20191203.4358","volume":"12","author":"L Wang","year":"2019","unstructured":"Wang, L., Lan, Y., Yue, X., Ling, K., Cen, Z., Cheng, Z., Liu, Y., Wang, J.: Vision-based adaptive variable rate spraying approach for unmanned aerial vehicles. Int. J. Agricul. Biol. Eng. 12, 18\u201326 (2019). https:\/\/doi.org\/10.25165\/j.ijabe.20191203.4358","journal-title":"Int. J. Agricul. Biol. Eng."},{"key":"2396_CR67","doi-asserted-by":"publisher","first-page":"106912","DOI":"10.1016\/j.compag.2022.106912","volume":"196","author":"P Chen","year":"2022","unstructured":"Chen, P., Xu, W., Zhan, Y., Wang, G., Yang, W., Lan, Y.: Determining application volume of unmanned aerial spraying systems for cotton defoliation using remote sensing images. Comput. Electron. Agric. 196, 106912 (2022). https:\/\/doi.org\/10.1016\/j.compag.2022.106912","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR68","doi-asserted-by":"publisher","unstructured":"Basso, M., Stocchero, D., Ventura Bayan\u00a0Henriques, R., Vian, A.L., Bredemeier, C., Konzen, A.A., Freitas, E.: Proposal for an embedded system architecture using a gndvi algorithm to support uav-based agrochemical spraying. Sensors 19(24) (2019). https:\/\/doi.org\/10.3390\/s19245397","DOI":"10.3390\/s19245397"},{"key":"2396_CR69","doi-asserted-by":"publisher","first-page":"106212","DOI":"10.1016\/j.cropro.2023.106212","volume":"167","author":"R Salcedo","year":"2023","unstructured":"Salcedo, R., S\u00e1nchez, E., Zhu, H., F\u00e0bregas, X., Garc\u00eda-Ruiz, F., Gil, E.: Evaluation of an electrostatic spray charge system implemented in three conventional orchard sprayers used on a commercial apple trees plantation. Crop Prot. 167, 106212 (2023). https:\/\/doi.org\/10.1016\/j.cropro.2023.106212","journal-title":"Crop Prot."},{"key":"2396_CR70","doi-asserted-by":"publisher","DOI":"10.1145\/3167132.3167237","author":"B Brandoli","year":"2018","unstructured":"Brandoli, B., Spadon, G., Arruda, M., Gon\u00e7alves, W., Carvalho, A., Rodrigues, J., Jr.: A smartphone application to measure the quality of pest control spraying machines via image analysis. (2018). https:\/\/doi.org\/10.1145\/3167132.3167237","journal-title":"A smartphone application to measure the quality of pest control spraying machines via image analysis."},{"key":"2396_CR71","doi-asserted-by":"publisher","first-page":"100460","DOI":"10.1016\/j.atech.2024.100460","volume":"8","author":"F Yan","year":"2024","unstructured":"Yan, F., Zhang, Y., Zhu, Y., Wang, Y., Niu, Z., Abdukamolovich, J.A.: An image segmentation of adhesive droplets based approach to assess the quality of pesticide spray. Smart Agricultural Technology 8, 100460 (2024). https:\/\/doi.org\/10.1016\/j.atech.2024.100460","journal-title":"Smart Agricultural Technology"},{"key":"2396_CR72","doi-asserted-by":"publisher","unstructured":"Chen, T., Meng, Y., Su, J., Liu, C.: Deep cnn based droplet deposition segmentation for spray distribution assessment. In: 2022 27th International Conference on Automation and Computing (ICAC), pp. 1\u20136 (2022). https:\/\/doi.org\/10.1109\/ICAC55051.2022.9911061","DOI":"10.1109\/ICAC55051.2022.9911061"},{"key":"2396_CR73","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/978-3-031-70955-5_2","volume-title":"Farm Machinery and Processes Management in Sustainable Agriculture","author":"AH Al-Ahmadi","year":"2024","unstructured":"Al-Ahmadi, A.H., Subr, A., Parafiniuk, S., Milanowski, M.: An image processing algorithm to address the problem of stains merge on water sensitive papers and its impact on the evaluation of spray quality indicators. In: Lorencowicz, E., Huyghebaert, B., Uziak, J. (eds.) Farm Machinery and Processes Management in Sustainable Agriculture, pp. 11\u201322. Springer, Cham (2024)"},{"key":"2396_CR74","doi-asserted-by":"publisher","unstructured":"Wang, L., Yue, X., Liu, Y., Wang, J., Wang, H.: An intelligent vision based sensing approach for spraying droplets deposition detection. Sensors 19(4) (2019). https:\/\/doi.org\/10.3390\/s19040933","DOI":"10.3390\/s19040933"},{"key":"2396_CR75","doi-asserted-by":"publisher","first-page":"18036","DOI":"10.7717\/peerj.18036","volume":"12","author":"Y Meng","year":"2024","unstructured":"Meng, Y., Liu, X., Chen, W., Du, X., Zhang, Y., Sun, R., Han, Y.: Evaluation of droplet deposition parameters based on the genetic-otsu algorithm. PeerJ 12, 18036 (2024). https:\/\/doi.org\/10.7717\/peerj.18036","journal-title":"PeerJ"},{"key":"2396_CR76","doi-asserted-by":"publisher","unstructured":"Liu, J., Yu, S., Liu, X., Lu, G., Xin, Z., Yuan, J.: Super-resolution semantic segmentation of droplet deposition image for low-cost spraying measurement. Agriculture 14(1) (2024). https:\/\/doi.org\/10.3390\/agriculture14010106","DOI":"10.3390\/agriculture14010106"},{"issue":"47","key":"2396_CR77","doi-asserted-by":"publisher","first-page":"14009","DOI":"10.1021\/acs.jafc.0c01835","volume":"68","author":"RF Menger","year":"2020","unstructured":"Menger, R.F., Bontha, M., Beveridge, J.R., Borch, T., Henry, C.S.: Fluorescent dye paper-based method for assessment of pesticide coverage on leaves and trees: A citrus grove case study. J. Agric. Food Chem. 68(47), 14009\u201314014 (2020). https:\/\/doi.org\/10.1021\/acs.jafc.0c01835","journal-title":"J. Agric. Food Chem."},{"key":"2396_CR78","doi-asserted-by":"publisher","first-page":"108632","DOI":"10.1016\/j.compag.2024.108632","volume":"217","author":"J Liu","year":"2024","unstructured":"Liu, J., Yu, S., Liu, X., Wang, Q., Cui, H., Zhu, Y., Yuan, J.: A novel optical shadow edge imaging method based fast in-situ measuring portable device for droplet deposition. Comput. Electron. Agric. 217, 108632 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.108632","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR79","doi-asserted-by":"publisher","unstructured":"Becce, L., Amin, S., Carabin, G., Mazzetto, F.: Preliminary spray nozzle characterization activities through shadowgraphy at the agroforestry innovation lab (afi-lab). In: 2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 136\u2013140 (2022). https:\/\/doi.org\/10.1109\/MetroAgriFor55389.2022.9965106","DOI":"10.1109\/MetroAgriFor55389.2022.9965106"},{"key":"2396_CR80","doi-asserted-by":"publisher","unstructured":"Narayanan, B.N., Gunasekaran, S., Ivarson, J., Maneck, L.: Deep learning algorithm for atomization characterization using shadowgraph images. In: NAECON 2021 - IEEE National Aerospace and Electronics Conference, pp. 74\u201379 (2021). https:\/\/doi.org\/10.1109\/NAECON49338.2021.9696443","DOI":"10.1109\/NAECON49338.2021.9696443"},{"key":"2396_CR81","doi-asserted-by":"publisher","DOI":"10.2514\/6.2022-0188","author":"J Ivarson","year":"2022","unstructured":"Ivarson, J., Maneck, L., Narayanan, B., Gunasekaran, S.: Agspray atomization characterization using deep learning. (2022). https:\/\/doi.org\/10.2514\/6.2022-0188","journal-title":"Agspray atomization characterization using deep learning."},{"key":"2396_CR82","doi-asserted-by":"publisher","unstructured":"Li, L., Zhang, R., Chen, L., Liu, B., Zhang, L., Tang, Q., Ding, C., Zhang, Z., Hewitt, A.J.: Spray drift evaluation with point clouds data of 3d lidar as a potential alternative to the sampling method. Front. Plant Sci. 13 (2022). https:\/\/doi.org\/10.3389\/fpls.2022.939733","DOI":"10.3389\/fpls.2022.939733"},{"key":"2396_CR83","doi-asserted-by":"publisher","first-page":"170819","DOI":"10.1016\/j.scitotenv.2024.170819","volume":"918","author":"L Li","year":"2024","unstructured":"Li, L., Zhang, R., Chen, L., Hewitt, A.J., He, X., Ding, C., Tang, Q., Liu, B.: Toward a remote sensing method based on commercial lidar sensors for the measurement of spray drift and potential drift reduction. Sci. Total Environ. 918, 170819 (2024). https:\/\/doi.org\/10.1016\/j.scitotenv.2024.170819","journal-title":"Sci. Total Environ."},{"key":"2396_CR84","doi-asserted-by":"publisher","first-page":"107325","DOI":"10.1016\/j.compag.2022.107325","volume":"202","author":"P Acharya","year":"2022","unstructured":"Acharya, P., Burgers, T., Nguyen, K.-D.: Ai-enabled droplet detection and tracking for agricultural spraying systems. Comput. Electron. Agric. 202, 107325 (2022). https:\/\/doi.org\/10.1016\/j.compag.2022.107325","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR85","doi-asserted-by":"publisher","unstructured":"Huynh, T.N., Burgers, T., Nguyen, K.-D.: Efficient real-time droplet tracking in crop-spraying systems. Agriculture 14(10) (2024). https:\/\/doi.org\/10.3390\/agriculture14101735","DOI":"10.3390\/agriculture14101735"},{"key":"2396_CR86","doi-asserted-by":"publisher","unstructured":"Yang, K., Gan, S., Lv, X., Liu, Z., Ma, L., Chang, Y.: Image processing-based droplet size detection. In: 2023 IEEE 3rd International Conference on Social Sciences and Intelligence Management (SSIM), pp. 200\u2013203 (2023). https:\/\/doi.org\/10.1109\/SSIM59263.2023.10469432","DOI":"10.1109\/SSIM59263.2023.10469432"},{"key":"2396_CR87","doi-asserted-by":"publisher","unstructured":"Privitera, S., Manetto, G., Pascuzzi, S., Pessina, D., Cerruto, E.: Drop size measurement techniques for agricultural sprays:a state-of-the-art review. Agronomy 13(3) (2023). https:\/\/doi.org\/10.3390\/agronomy13030678","DOI":"10.3390\/agronomy13030678"},{"key":"2396_CR88","doi-asserted-by":"publisher","unstructured":"Mangado, J., Arazuri, S., Arnal, P., Jar\u00e9n, C., L\u00f3pez, A.: Measuring the accuracy of a pesticide treatment by an image analyzer. Procedia Technology 8, 498\u2013502 (2013) https:\/\/doi.org\/10.1016\/j.protcy.2013.11.066. 6th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2013)","DOI":"10.1016\/j.protcy.2013.11.066"},{"issue":"1","key":"2396_CR89","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.compag.2011.01.003","volume":"76","author":"H Zhu","year":"2011","unstructured":"Zhu, H., Salyani, M., Fox, R.D.: A portable scanning system for evaluation of spray deposit distribution. Comput. Electron. Agric. 76(1), 38\u201343 (2011). https:\/\/doi.org\/10.1016\/j.compag.2011.01.003","journal-title":"Comput. Electron. Agric."},{"key":"2396_CR90","first-page":"266","volume":"57","author":"R Castrej\u00f3n-Garc\u00eda","year":"2011","unstructured":"Castrej\u00f3n-Garc\u00eda, R., Castrejon-Pita, R., Martin, G.D., Hutchings, I.M.: The shadowgraph imaging technique and its modern application to fluid jets and drops. Revista mexicana de f\u00edsica 57, 266\u2013275 (2011)","journal-title":"Revista mexicana de f\u00edsica"},{"key":"2396_CR91","doi-asserted-by":"publisher","unstructured":"Vatavuk, I., Vasiljevi\u0107, G., Kova\u010di\u0107, Z.: Task space model predictive control for vineyard spraying with a mobile manipulator. Agriculture 12(3) (2022). https:\/\/doi.org\/10.3390\/agriculture12030381","DOI":"10.3390\/agriculture12030381"},{"key":"2396_CR92","doi-asserted-by":"publisher","unstructured":"Baltazar, A., Santos, F.N., Paulo\u00a0Moreira, A., Soares, S.P., Reis, M.J.C.S., Cunha, J.B.: Modelling and control of a trailer sprayer for precision spraying. In: 2024 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 171\u2013176 (2024). https:\/\/doi.org\/10.1109\/ICARSC61747.2024.10535952","DOI":"10.1109\/ICARSC61747.2024.10535952"},{"issue":"2","key":"2396_CR93","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.baae.2009.12.001","volume":"11","author":"F Geiger","year":"2010","unstructured":"Geiger, F., Bengtsson, J., Berendse, F., Weisser, W.W., Emmerson, M., Morales, M.B., Ceryngier, P., Liira, J., Tscharntke, T., Winqvist, C., Eggers, S., Bommarco, R., P\u00e4rt, T., Bretagnolle, V., Plantegenest, M., Clement, L.W., Dennis, C., Palmer, C., O\u00f1ate, J.J., Guerrero, I., Hawro, V., Aavik, T., Thies, C., Flohre, A., H\u00e4nke, S., Fischer, C., Goedhart, P.W., Inchausti, P.: Persistent negative effects of pesticides on biodiversity and biological control potential on european farmland. Basic Appl. Ecol. 11(2), 97\u2013105 (2010). https:\/\/doi.org\/10.1016\/j.baae.2009.12.001","journal-title":"Basic Appl. Ecol."},{"issue":"1","key":"2396_CR94","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17221\/29\/2019-HORTSCI","volume":"47","author":"JM Mia","year":"2020","unstructured":"Mia, J.M., Massetani, F., Murri, G., Neri, D.: Sustainable alternatives to chemicals for weed control in the orchard - a review. Hortic. Sci. 47(1), 1\u201312 (2020). https:\/\/doi.org\/10.17221\/29\/2019-HORTSCI","journal-title":"Hortic. Sci."},{"key":"2396_CR95","unstructured":"Kanissery, R., Brewer, M., Kadyampakeni, D., Strauss, S.: Row-middle weed management methods 2020 (2020)"},{"key":"2396_CR96","doi-asserted-by":"publisher","first-page":"156441","DOI":"10.1016\/j.scitotenv.2022.156441","volume":"838","author":"M Pradel","year":"2022","unstructured":"Pradel, M., de Fays, M., Seguineau, C.: Comparative life cycle assessment of intra-row and inter-row weeding practices using autonomous robot systems in french vineyards. Sci. Total Environ. 838, 156441 (2022). https:\/\/doi.org\/10.1016\/j.scitotenv.2022.156441","journal-title":"Sci. Total Environ."},{"key":"2396_CR97","doi-asserted-by":"publisher","unstructured":"Mia, M.J., Monaci, E., Murri, G., Massetani, F., Facchi, J., Neri, D.: Soil nitrogen and weed biodiversity: An assessment under two orchard floor management practices in a nitrogen vulnerable zone in italy. Horticulturae 6(4) (2020). https:\/\/doi.org\/10.3390\/horticulturae6040096","DOI":"10.3390\/horticulturae6040096"},{"key":"2396_CR98","doi-asserted-by":"publisher","unstructured":"Xia, J., Ganbold, U., Zhang, Y., Sasaki, H., Shima, D., Akashi, T.: Local texture based borderline detection of mowing. In: 2019 Nicograph International (NicoInt), pp. 5\u20138 (2019). https:\/\/doi.org\/10.1109\/NICOInt.2019.00008","DOI":"10.1109\/NICOInt.2019.00008"},{"key":"2396_CR99","doi-asserted-by":"crossref","unstructured":"Kim, Y., Kim, S., Yajima, Y., Irizarry, J., Cho, Y.K.: Development of framework for highway lawn condition monitoring using uav images. In: Linner, T., Soto, B., Hu, R., Brilakis, I., Bock, T., Pan, W., Carbonari, A., Castro, D., Mesa, H., Feng, C., Fischer, M., Brosque, C., Gonzalez, V., Hall, D., Ng, M.S., Kamat, V., Liang, C.-J., Lafhaj, Z., Pan, W., Pan, M., Zhu, Z. (eds.) Proceedings of the 39th International Symposium on Automation and Robotics in Construction, pp. 444\u2013450. International Association for Automation and Robotics in Construction (IAARC), Bogot\u00e1, Colombia (2022). https:\/\/doi.org\/10.22260\/ISARC2022\/0061","DOI":"10.22260\/ISARC2022\/0061"},{"key":"2396_CR100","doi-asserted-by":"publisher","first-page":"113145","DOI":"10.1016\/j.rse.2022.113145","volume":"280","author":"M De Vroey","year":"2022","unstructured":"De Vroey, M., de Vendictis, L., Zavagli, M., Bontemps, S., Heymans, D., Radoux, J., Koetz, B., Defourny, P.: Mowing detection using sentinel-1 and sentinel-2 time series for large scale grassland monitoring. Remote Sens. Environ. 280, 113145 (2022). https:\/\/doi.org\/10.1016\/j.rse.2022.113145","journal-title":"Remote Sens. Environ."},{"key":"2396_CR101","doi-asserted-by":"publisher","first-page":"113680","DOI":"10.1016\/j.rse.2023.113680","volume":"295","author":"A-K Holtgrave","year":"2023","unstructured":"Holtgrave, A.-K., Lobert, F., Erasmi, S., R\u00f6der, N., Kleinschmit, B.: Grassland mowing event detection using combined optical, sar, and weather time series. Remote Sens. Environ. 295, 113680 (2023). https:\/\/doi.org\/10.1016\/j.rse.2023.113680","journal-title":"Remote Sens. Environ."},{"key":"2396_CR102","doi-asserted-by":"crossref","unstructured":"Griffiths, P., Nendel, C., Pickert, J., Hostert, P.: Towards national-scale characterization of grassland use intensity from integrated sentinel-2 and landsat time series. Remote Sensing of Environment. Time Series Analysis with High Spatial Resolution Imagery 238, 111124 (2020). https:\/\/doi.org\/10.1016\/j.rse.2019.03.017","DOI":"10.1016\/j.rse.2019.03.017"},{"key":"2396_CR103","doi-asserted-by":"publisher","first-page":"112795","DOI":"10.1016\/j.rse.2021.112795","volume":"269","author":"M Schwieder","year":"2022","unstructured":"Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., Hostert, P.: Mapping grassland mowing events across germany based on combined sentinel-2 and landsat 8 time series. Remote Sens. Environ. 269, 112795 (2022). https:\/\/doi.org\/10.1016\/j.rse.2021.112795","journal-title":"Remote Sens. Environ."}],"container-title":["Journal of Intelligent &amp; Robotic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10846-026-02396-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T11:08:32Z","timestamp":1776683312000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10846-026-02396-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":103,"alternative-id":["2396"],"URL":"https:\/\/doi.org\/10.1007\/s10846-026-02396-8","relation":{},"ISSN":["1573-0409"],"issn-type":[{"value":"1573-0409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"27 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}