{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:53:39Z","timestamp":1776207219440,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00521-024-09582-5","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T20:02:57Z","timestamp":1709841777000},"page":"9591-9604","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A deep learning-based framework for object recognition in ecological environments with dense focal loss and occlusion"],"prefix":"10.1007","volume":"36","author":[{"given":"Muhammad Munir","family":"Afsar","sequence":"first","affiliation":[]},{"given":"Asim Dilawar","family":"Bakhshi","sequence":"additional","affiliation":[]},{"given":"Ejaz","family":"Hussain","sequence":"additional","affiliation":[]},{"given":"Javed","family":"Iqbal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"9582_CR1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.14257\/ijast.2017.103.05","volume":"103","author":"A Azam","year":"2017","unstructured":"Azam A, Shafique M (2017) Agriculture in Pakistan and its impact on economy. A Review. Inter J Adv Sci Technol 103:47\u201360","journal-title":"Inter J Adv Sci Technol"},{"issue":"2","key":"9582_CR2","first-page":"1","volume":"4","author":"M Usman","year":"2016","unstructured":"Usman M (2016) Contribution of agriculture sector in the GDP growth rate of Pakistan. J Global Econ 4(2):1\u20133","journal-title":"J Global Econ"},{"key":"9582_CR3","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/1353691","author":"J Xue","year":"2017","unstructured":"Xue J, Su B et al (2017) Significant remote sensing vegetation indices: a review of developments and applications. J Sens. https:\/\/doi.org\/10.1155\/2017\/1353691","journal-title":"J Sens"},{"issue":"4","key":"9582_CR4","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.3390\/s8042136","volume":"8","author":"EP Glenn","year":"2008","unstructured":"Glenn EP, Huete AR, Nagler PL, Nelson SG (2008) Relationship between remotely-sensed vegetation indices, canopy attributes, and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8(4):2136\u20132160","journal-title":"Sensors"},{"key":"9582_CR5","doi-asserted-by":"publisher","first-page":"1130448","DOI":"10.3389\/fenvs.2023.1130448","volume":"11","author":"S Wang","year":"2023","unstructured":"Wang S, Fu G (2023) Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands. Front Environ Sci 11:1130448","journal-title":"Front Environ Sci"},{"issue":"1","key":"9582_CR6","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s13007-023-00981-8","volume":"19","author":"JD Stamford","year":"2023","unstructured":"Stamford JD, Vialet-Chabrand S, Cameron I, Lawson T (2023) Development of an accurate low cost NDVI imaging system for assessing plant health. Plant Methods 19(1):9","journal-title":"Plant Methods"},{"key":"9582_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2019.111402","volume":"236","author":"M Weiss","year":"2020","unstructured":"Weiss M, Jacob F, Duveiller G (2020) Remote sensing for agricultural applications: a meta-review. Remote Sens Environ 236:111402","journal-title":"Remote Sens Environ"},{"issue":"2","key":"9582_CR8","doi-asserted-by":"publisher","first-page":"354","DOI":"10.3390\/rs15020354","volume":"15","author":"E Omia","year":"2023","unstructured":"Omia E, Bae H, Park E, Kim MS, Baek I, Kabenge I, Cho B-K (2023) Remote sensing in field crop monitoring: a comprehensive review of sensor systems, data analyses and recent advances. Remote Sens 15(2):354","journal-title":"Remote Sens"},{"issue":"8","key":"9582_CR9","doi-asserted-by":"publisher","first-page":"2067","DOI":"10.3390\/rs15082067","volume":"15","author":"S Zhang","year":"2023","unstructured":"Zhang S, Yu J, Xu H, Qi S, Luo J, Huang S, Liao K, Huang M (2023) Mapping the age of subtropical secondary forest using dense landsat time series data: an ensemble model. Remote Sens 15(8):2067","journal-title":"Remote Sens"},{"key":"9582_CR10","unstructured":"Gupta P, Sharma R (1990) Application of satellite remote sensing technique in delineation and hectarage estimation of mango orchards in parts of uttar pradesh, india. In: Proceedings of the GIS development, Uttar Pradesh, India, pp 12\u201314"},{"key":"9582_CR11","unstructured":"Stussi N, Liew SC, Kwoh LK, Lim H, Nichol J, Goh KC (1997) Landcover classification using ERS SAR\/INSAR data on the coastal region of central sumatra"},{"key":"9582_CR12","unstructured":"N\u2019Doume C, Lachenaud P, Hussard A, Nguyen H, Flori A (2000) Etude de faisabilit\u00e9 pour l\u2019\u00e9laboration d\u2019une cartographie statistique d\u2019inventaire des vergers caf\u00e9 et cacao en c\u00f4te d\u2019ivoire par t\u00e9l\u00e9d\u00e9tection satellitale"},{"issue":"2","key":"9582_CR13","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/S0304-4238(01)00321-1","volume":"93","author":"I Yadav","year":"2002","unstructured":"Yadav I, Rao NS, Reddy B, Rawal R, Srinivasan V, Sujatha N, Bhattacharya C, Rao PN, Ramesh K, Elango S (2002) Acreage and production estimation of mango orchards using Indian remote sensing (IRS) satellite data. Sci Hortic 93(2):105\u2013123","journal-title":"Sci Hortic"},{"key":"9582_CR14","first-page":"1706","volume":"91","author":"C Palaniswami","year":"2006","unstructured":"Palaniswami C, Upadhyay A, Maheswarappa H (2006) Spectral mixture analysis for subpixel classification of coconut. Curr Sci 91:1706\u20131711","journal-title":"Curr Sci"},{"key":"9582_CR15","unstructured":"Sharma A, Panigrahy S (2007) Apple orchard characterization using remote sensing and GIS in shimla district of himachal pradesh. In: Proceedings of remote sensing and photogrammetry annual conference 2007, pp 11\u201314. Citeseer"},{"key":"9582_CR16","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"9582_CR17","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"issue":"11","key":"9582_CR18","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.3390\/rs11111309","volume":"11","author":"BG Weinstein","year":"2019","unstructured":"Weinstein BG, Marconi S, Bohlman S, Zare A, White E (2019) Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sens 11(11):1309","journal-title":"Remote Sens"},{"issue":"1","key":"9582_CR19","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.rse.2005.04.013","volume":"97","author":"KJ Salovaara","year":"2005","unstructured":"Salovaara KJ, Thessler S, Malik RN, Tuomisto H (2005) Classification of Amazonian primary rain forest vegetation using Landsat ETM+ satellite imagery. Remote Sens Environ 97(1):39\u201351","journal-title":"Remote Sens Environ"},{"issue":"17","key":"9582_CR20","doi-asserted-by":"publisher","first-page":"4725","DOI":"10.1080\/01431161.2010.494184","volume":"32","author":"Y Ke","year":"2011","unstructured":"Ke Y, Quackenbush LJ (2011) A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. Int J Remote Sens 32(17):4725\u20134747","journal-title":"Int J Remote Sens"},{"issue":"20","key":"9582_CR21","doi-asserted-by":"publisher","first-page":"5827","DOI":"10.1080\/01431161.2010.507790","volume":"32","author":"M Larsen","year":"2011","unstructured":"Larsen M, Eriksson M, Descombes X, Perrin G, Brandtberg T, Gougeon FA (2011) Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions. Int J Remote Sens 32(20):5827\u20135852","journal-title":"Int J Remote Sens"},{"issue":"9","key":"9582_CR22","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.3390\/rs11091086","volume":"11","author":"M Aubry-Kientz","year":"2019","unstructured":"Aubry-Kientz M, Dutrieux R, Ferraz A, Saatchi S, Hamraz H, Williams J, Coomes D, Piboule A, Vincent G (2019) A comparative assessment of the performance of individual tree crowns delineation algorithms from ALS data in tropical forests. Remote Sens 11(9):1086","journal-title":"Remote Sens"},{"key":"9582_CR23","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.isprsjprs.2023.05.032","volume":"202","author":"W Han","year":"2023","unstructured":"Han W, Zhang X, Wang Y, Wang L, Huang X, Li J, Wang S, Chen W, Li X, Feng R et al (2023) A survey of machine learning and deep learning in remote sensing of geological environment: challenges, advances, and opportunities. ISPRS J Photogramm Remote Sens 202:87\u2013113","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"9582_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.104046","volume":"104","author":"P Mittal","year":"2020","unstructured":"Mittal P, Singh R, Sharma A (2020) Deep learning-based object detection in low-altitude UAV datasets: a survey. Image Vis Comput 104:104046","journal-title":"Image Vis Comput"},{"key":"9582_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2022.100083","volume":"3","author":"A Ahmad","year":"2023","unstructured":"Ahmad A, Saraswat D, El Gamal A (2023) A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agric Technol 3:100083","journal-title":"Smart Agric Technol"},{"issue":"1","key":"9582_CR26","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3390\/rs9010022","volume":"9","author":"W Li","year":"2017","unstructured":"Li W, Fu H, Yu L, Cracknell A (2017) Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sens 9(1):22","journal-title":"Remote Sens"},{"issue":"4","key":"9582_CR27","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3390\/drones2040039","volume":"2","author":"O Csillik","year":"2018","unstructured":"Csillik O, Cherbini J, Johnson R, Lyons A, Kelly M (2018) Identification of citrus trees from unmanned aerial vehicle imagery using convolutional neural networks. Drones 2(4):39","journal-title":"Drones"},{"issue":"4","key":"9582_CR28","doi-asserted-by":"publisher","first-page":"649","DOI":"10.3390\/rs10040649","volume":"10","author":"E Ayrey","year":"2018","unstructured":"Ayrey E, Hayes DJ (2018) The use of three-dimensional convolutional neural networks to interpret LiDAR for forest inventory. Remote Sens 10(4):649","journal-title":"Remote Sens"},{"issue":"12","key":"9582_CR29","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.3390\/rs9121220","volume":"9","author":"E Guirado","year":"2017","unstructured":"Guirado E, Tabik S, Alcaraz-Segura D, Cabello J, Herrera F (2017) Deep-learning versus OBIA for scattered shrub detection with google earth imagery: Ziziphus lotus as case study. Remote Sens 9(12):1220","journal-title":"Remote Sens"},{"issue":"11","key":"9582_CR30","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.3390\/s16111915","volume":"16","author":"M Stein","year":"2016","unstructured":"Stein M, Bargoti S, Underwood J (2016) Image based mango fruit detection, localisation and yield estimation using multiple view geometry. Sensors 16(11):1915","journal-title":"Sensors"},{"issue":"2","key":"9582_CR31","doi-asserted-by":"publisher","first-page":"184","DOI":"10.5958\/0974-0112.2017.00040.8","volume":"74","author":"A Nagaraja","year":"2017","unstructured":"Nagaraja A, Sahoo R, Usha K, Gupta V (2017) Estimation of mango growing areas using remote sensing. Indian J Hortic 74(2):184\u2013188","journal-title":"Indian J Hortic"},{"issue":"12","key":"9582_CR32","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.3390\/rs10121866","volume":"10","author":"MM Rahman","year":"2018","unstructured":"Rahman MM, Robson A, Bristow M (2018) Exploring the potential of high resolution worldview-3 imagery for estimating yield of mango. Remote Sens 10(12):1866","journal-title":"Remote Sens"},{"key":"9582_CR33","unstructured":"Agaradahalli\u00a0Gurumurthy V, Kestur R, Narasipura O (2019) Mango tree net\u2013a fully convolutional network for semantic segmentation and individual crown detection of mango trees. arXiv preprint arXiv:1907.06915"},{"key":"9582_CR34","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.engappai.2018.09.011","volume":"77","author":"R Kestur","year":"2019","unstructured":"Kestur R, Meduri A, Narasipura O (2019) MangoNet: a deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard. Eng Appl Artif Intell 77:59\u201369","journal-title":"Eng Appl Artif Intell"},{"issue":"1\u20132","key":"9582_CR35","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","volume":"83","author":"A Huete","year":"2002","unstructured":"Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the modis vegetation indices. Remote Sens Environ 83(1\u20132):195\u2013213","journal-title":"Remote Sens Environ"},{"key":"9582_CR36","doi-asserted-by":"publisher","DOI":"10.1029\/2002GL016450","author":"AA Gitelson","year":"2003","unstructured":"Gitelson AA, Vi\u00f1a A, Arkebauer TJ, Rundquist DC, Keydan G, Leavitt B (2003) Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys Res Lett. https:\/\/doi.org\/10.1029\/2002GL016450","journal-title":"Geophys Res Lett"},{"issue":"2","key":"9582_CR37","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","volume":"48","author":"J Qi","year":"1994","unstructured":"Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48(2):119\u2013126","journal-title":"Remote Sens Environ"},{"issue":"3","key":"9582_CR38","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","volume":"25","author":"AR Huete","year":"1988","unstructured":"Huete AR (1988) A soil-adjusted vegetation index (savi). Remote Sens Environ 25(3):295\u2013309","journal-title":"Remote Sens Environ"},{"issue":"1","key":"9582_CR39","doi-asserted-by":"publisher","first-page":"28","DOI":"10.2307\/1942049","volume":"5","author":"JA Gamon","year":"1995","unstructured":"Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Penuelas J, Valentini R (1995) Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol Appl 5(1):28\u201341","journal-title":"Ecol Appl"},{"issue":"1","key":"9582_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11676-020-01155-1","volume":"32","author":"S Huang","year":"2021","unstructured":"Huang S, Tang L, Hupy JP, Wang Y, Shao G (2021) A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J For Res 32(1):1\u20136","journal-title":"J For Res"},{"key":"9582_CR41","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda M, Maki A, Mazurowski MA (2018) A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw 106:249\u2013259","journal-title":"Neural Netw"},{"key":"9582_CR42","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"9582_CR43","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"9582_CR44","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9582_CR45","unstructured":"Gaiser H, Vries M, Williamson A, Henon Y, Morariu M, Lacatusu V, Liscio E, Fang W, Clark M, Sande M, et al (2019) fizyr\/keras-retinanet 0.2"},{"issue":"3","key":"9582_CR46","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1080\/02693799608902083","volume":"10","author":"M Morad","year":"1996","unstructured":"Morad M, Chalmers A, O\u2019regan P (1996) The role of root-mean-square error in the geo-transformation of images in GIS. Int J Geogr Inf Sci 10(3):347\u2013353","journal-title":"Int J Geogr Inf Sci"},{"issue":"10","key":"9582_CR47","doi-asserted-by":"publisher","first-page":"0259283","DOI":"10.1371\/journal.pone.0259283","volume":"16","author":"W Wu","year":"2021","unstructured":"Wu W, Liu H, Li L, Long Y, Wang X, Wang Z, Li J, Chang Y (2021) Application of local fully convolutional neural network combined with YOLO v5 algorithm in small target detection of remote sensing image. PLoS ONE 16(10):0259283","journal-title":"PLoS ONE"},{"key":"9582_CR48","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pp 21\u201337. Springer","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"9582_CR49","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09582-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09582-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09582-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T05:15:39Z","timestamp":1715404539000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09582-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,7]]},"references-count":49,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9582"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09582-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,7]]},"assertion":[{"value":"11 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2024","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 have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}