{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:02:22Z","timestamp":1791392542846,"version":"4.3.3"},"reference-count":141,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union NextGenerationEU\/PRTR","award":["10.13039\/501100011033"],"award-info":[{"award-number":["10.13039\/501100011033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Automated biological counting is essential for scaling wildlife monitoring and biodiversity assessments, as manual processing currently limits analytical effort and scalability. This review evaluates the integration of deep learning and computer vision across diverse acquisition platforms, including camera traps, unmanned aerial vehicles (UAVs), and remote sensing. Methodological paradigms ranging from Convolutional Neural Networks (CNNs) and one-stage detectors like You Only Look Once (YOLO) to recent transformer-based architectures and hybrid models are examined. The literature shows that these methods consistently achieve high accuracy\u2014often exceeding 95%\u2014across various taxa, including insect pests, aquatic organisms, terrestrial vegetation, and forest ecosystems. However, persistent challenges such as object occlusion, cryptic species differentiation, and the scarcity of high-quality, labeled datasets continue to hinder fully automated workflows. We conclude that while automated counting has fundamentally increased data throughput, future advancements must focus on enhancing model generalization through self-supervised learning and improved data augmentation techniques. These developments are critical for transitioning from experimental models to robust, operational tools for global ecological monitoring and conservation efforts.<\/jats:p>","DOI":"10.3390\/jimaging12020088","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T12:59:16Z","timestamp":1771419556000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1440-8862","authenticated-orcid":false,"given":"Aaron","family":"G\u00e1lvez-Salido","sequence":"first","affiliation":[{"name":"Departamento de Biolog\u00eda y Geolog\u00eda, \u00c1rea de Gen\u00e9tica, Universidad de Almer\u00eda, 04120 Almer\u00eda, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1738-5325","authenticated-orcid":false,"given":"Francisca","family":"Robles","sequence":"additional","affiliation":[{"name":"Departamento de Gen\u00e9tica, Universidad de Granada, 18071 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4332-2312","authenticated-orcid":false,"given":"Rodrigo J.","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Departamento de Ecolog\u00eda, Universidad de Granada, 18071 Granada, Spain"},{"name":"Research Unit Modeling Nature (MNat), Universidad de Granada, 18071 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3772-4374","authenticated-orcid":false,"given":"Roberto de la","family":"Herr\u00e1n","sequence":"additional","affiliation":[{"name":"Departamento de Gen\u00e9tica, Universidad de Granada, 18071 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9980-9237","authenticated-orcid":false,"given":"Carmelo","family":"Ruiz Rej\u00f3n","sequence":"additional","affiliation":[{"name":"Departamento de Gen\u00e9tica, Universidad de Granada, 18071 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6147-3640","authenticated-orcid":false,"given":"Rafael","family":"Navajas-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Departamento de Gen\u00e9tica, Universidad de Granada, 18071 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Delisle, Z.J., Flaherty, E.A., Nobbe, M.R., Wzientek, C.M., and Swihart, R.K. (2021). Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation. Front. Ecol. Evol., 9.","DOI":"10.3389\/fevo.2021.617996"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"O\u2019Connell, A.F., Nichols, J.D., and Karanth, K.U. (2011). Camera Traps in Animal Ecology: Methods and Analyses, Springer.","DOI":"10.1007\/978-4-431-99495-4"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1007\/s13280-015-0713-1","article-title":"Limitations of recreational camera traps for wildlife management and conservation research: A practitioner\u2019s perspective","volume":"44","author":"Newey","year":"2015","journal-title":"Ambio"},{"key":"ref_4","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"E5716","DOI":"10.1073\/pnas.1719367115","article-title":"Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning","volume":"115","author":"Norouzzadeh","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1111\/2041-210X.13120","article-title":"Machine learning to classify animal species in camera trap images: Applications in ecology","volume":"10","author":"Tabak","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vermeulen, C., Lejeune, P., Lisein, J., Sawadogo, P., and Bouche, P. (2013). Unmanned aerial survey of elephants. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0054700"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"16214","DOI":"10.1002\/ece3.8302","article-title":"Drones, automatic counting tools, and artificial neural networks in wildlife population censusing","volume":"11","author":"Marchowski","year":"2021","journal-title":"Ecol. Evol."},{"key":"ref_9","first-page":"1","article-title":"Hoofed animal detection in UAV thermal images using balanced random forest and CNN features","volume":"120","year":"2025","journal-title":"Rep. Geod. Geoinform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s10980-024-01991-0","article-title":"A review of UAS-based estimation of forest traits and characteristics in landscape ecology","volume":"40","author":"Spiers","year":"2025","journal-title":"Landsc. Ecol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dutton, P.H., Komoroske, L.M., Bejder, L., and Meekan, M. (2019). Integrating emerging technologies into marine megafauna conservation management. Front. Mar. Sci., 6.","DOI":"10.3389\/fmars.2019.00693"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mulero-Pazmany, M., Stolper, R., van Essen, L.D., Negro, J.J., and Sassen, T. (2014). Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0083873"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Afridi, S., Laporte-Devylder, L., Maalouf, G., Kline, J.M., Penny, S.G., Hlebowicz, K., Cawthorne, D., and Lundquist, U.P.S. (2025). Impact of Drone Disturbances on Wildlife: A Review. Drones, 9.","DOI":"10.20944\/preprints202503.0892.v2"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9647","DOI":"10.3390\/s101109647","article-title":"Remote sensing of ecology, biodiversity and conservation: A review from the perspective of remote sensing specialists","volume":"10","author":"Wang","year":"2010","journal-title":"Sensors"},{"key":"ref_15","first-page":"101733","article-title":"Contribution of high-resolution remote sensing to spatial ecology of forest ecosystems at the single tree level: A systematic review","volume":"40","author":"Erfanifard","year":"2025","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103363","DOI":"10.1016\/j.ecoinf.2025.103363","article-title":"A high-resolution spatial modelling framework for landscape-level, strategic conservation planning","volume":"91","author":"Foxley","year":"2025","journal-title":"Ecol. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guo, Y., Mokany, K., Ong, C., Moghadam, P., Ferrier, S., and Levick, S.R. (2022). Quantitative assessment of DESIS hyperspectral data for plant biodiversity estimation in Australia. arXiv.","DOI":"10.1109\/IGARSS46834.2022.9883033"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16157","DOI":"10.1038\/s41598-021-95713-0","article-title":"Hyperspectral data as a biodiversity screening tool can differentiate among diverse Neotropical fishes","volume":"11","author":"Kolmann","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, T., and Zhong, S. (2024). Advances in optical and thermal remote sensing of vegetative drought and phenology. Remote Sens., 16.","DOI":"10.3390\/rs16224209"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1111\/2041-210X.70096","article-title":"Using aerial thermography to map terrestrial thermal environments in unprecedented detail","volume":"16","author":"Ratia","year":"2025","journal-title":"Methods Ecol. Evol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5244","DOI":"10.1038\/s41598-022-09376-6","article-title":"CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals","volume":"12","author":"Johansen","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1002\/rse2.239","article-title":"SmallSats: A new technological frontier in ecology and conservation?","volume":"8","author":"Curnick","year":"2022","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kacic, P., and Kuenzer, C. (2022). Forest biodiversity monitoring based on remotely sensed spectral diversity: A review. Remote Sens., 14.","DOI":"10.3390\/rs14215363"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.17520\/biods.2019166","article-title":"Advances in LiDAR technology in the field of animal ecology","volume":"27","author":"Li","year":"2019","journal-title":"Biodivers. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113326","DOI":"10.1016\/j.rse.2022.113326","article-title":"Habitat highs and lows: Using terrestrial and UAV LiDAR for modelling avian species richness and abundance in a restored woodland","volume":"285","author":"Shokirov","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.procs.2018.05.069","article-title":"Conceptual Understanding of Convolutional Neural Network\u2014A Deep Learning Approach","volume":"132","author":"Indolia","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"40","DOI":"10.54254\/2755-2721\/104\/20241062","article-title":"Overview of object detection based on deep learning","volume":"104","author":"Zhang","year":"2024","journal-title":"Appl. Comput. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","first-page":"21","article-title":"SSD: Single Shot MultiBox Detector","volume":"Volume 9905","author":"Liu","year":"2016","journal-title":"Computer Vision\u2014ECCV 2016, Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lu\u010di\u0107, M., and Schmid, C. (2021, January 11\u201317). ViViT: A Video Vision Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"ref_36","first-page":"213","article-title":"End-to-End Object Detection with Transformers","volume":"Volume 12346","author":"Carion","year":"2020","journal-title":"Computer Vision\u2014ECCV 2020, Proceedings of the 16th European Conference, Glasgow, UK, 23\u201328 August 2020"},{"key":"ref_37","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable DETR: Deformable Transformers for End-to-End Object Detection. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e2002545117","DOI":"10.1073\/pnas.2002545117","article-title":"Deep learning and computer vision will transform entomology","volume":"118","author":"Bjerge","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1111\/2041-210X.13576","article-title":"Robust ecological analysis of camera trap data labelled by a machine learning model","volume":"12","author":"Whytock","year":"2021","journal-title":"Methods Ecol. Evol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/978-3-319-70353-4_13","article-title":"Deep Learning on Underwater Marine Object Detection: A Survey","volume":"Volume 10617","author":"Moniruzzaman","year":"2017","journal-title":"Advanced Concepts for Intelligent Vision Systems"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1111\/2041-210X.12973","article-title":"How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals","volume":"9","author":"Hollings","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e13152","DOI":"10.7717\/peerj.13152","article-title":"Computational bioacoustics with deep learning: A review and roadmap","volume":"10","author":"Stowell","year":"2022","journal-title":"PeerJ"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1111\/2041-210X.13256","article-title":"Applications for deep learning in ecology","volume":"10","author":"Christin","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_44","unstructured":"Gao, G., Gao, J., Liu, Q., Wang, Q., and Wang, Y. (2020). CNN-based density estimation and crowd counting: A survey. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.3390\/agronomy15092022","article-title":"YOLO-LCE: A Lightweight YOLOv8 Model for Agricultural Pest Detection","volume":"15","author":"Cen","year":"2025","journal-title":"Agronomy"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, X., Bu, J., Zhou, X., and Wang, X. (2023). Automatic pest identification system in the greenhouse based on deep learning and machine vision. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1255719"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tang, R., Jun, T., Chu, Q., and Sun, Y. (2025). Small object detection in agriculture: A case study on durian orchards using EN-YOLO and thermal fusion. Plants, 14.","DOI":"10.3390\/plants14172619"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"13456","DOI":"10.1038\/s41598-021-92891-9","article-title":"Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection","volume":"11","author":"Goodwin","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8921","DOI":"10.1038\/s41598-023-37574-3","article-title":"Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system","volume":"13","author":"Kittichai","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_50","first-page":"1347","article-title":"Deep learning-driven mosquito species identification using YOLOv5 for disease monitoring and control","volume":"103","author":"Begum","year":"2025","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Molina-Rotger, M., Mor\u00e1n, A., Miranda, M.A., and Alorda-Ladaria, B. (2023). Remote fruit fly detection using computer vision and machine learning-based electronic trap. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1241576"},{"key":"ref_52","first-page":"45","article-title":"Image processing algorithm for improving the identification of patterns on Diptera wings","volume":"13","author":"Augot","year":"2023","journal-title":"Int. J. Comput. Vis. Image Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s10462-025-11457-7","article-title":"Automated detection of cotton aphids (Aphis gossypii Glover) using a bi-enhanced attention mechanism and domain-adaptive transfer learning strategy","volume":"59","author":"Huang","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s13355-024-00873-w","article-title":"Image recognition-based deep learning model for identifying the developmental stages of Acyrthosiphon pisum (Hemiptera: Aphididae)","volume":"59","author":"Masuko","year":"2024","journal-title":"Appl. Entomol. Zool."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bjerge, K., Nielsen, J.B., Sepasgozar, S.M.E., and H\u00f8ye, T.T. (2021). An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning. Sensors, 21.","DOI":"10.3390\/s21020343"},{"key":"ref_56","unstructured":"Yadav, D. (2023). Butterfly and Moth Species Detection and Classification Using Deep Learning, National College of Ireland."},{"key":"ref_57","first-page":"1","article-title":"Surveillance of insect pests in corn crops: Monitoring and counting using electronic traps","volume":"19","year":"2025","journal-title":"Vis. Electron."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1093\/sysbio\/syz014","article-title":"Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks","volume":"68","author":"Valan","year":"2019","journal-title":"Syst. Biol."},{"key":"ref_59","unstructured":"Liu, F., Rayeed, S.M., Stevens, S., East, A., Chiang, C.H., Lee, C., Yi, D., Yang, J., Naik, T., and Wang, Z. (2025). BeetleFlow: An integrative deep learning pipeline for beetle image processing. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Barboza da Silva, C., Naves Silva, A.A., Barroso, G., Takao Yamamoto, P., Arthur, V., Motta Toledo, C.F., and de Ara\u00fajo Mastrangelo, T. (2021). Convolutional neural networks using enhanced radiographs for real-time detection of Sitophilus zeamais in maize grain. Foods, 10.","DOI":"10.3390\/foods10040879"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Karakan, A. (2024). Potato beetle detection with real-time and deep learning. Processes, 12.","DOI":"10.3390\/pr12092038"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Spiesman, B.J., Gratton, C., Gratton, E., and Hines, H. (2024). Deep learning for identifying bee species from images of wings and pinned specimens. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0303383"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10841-025-00691-7","article-title":"Image-based South Asian bee species identification: A machine learning approach","volume":"29","author":"Srinivas","year":"2025","journal-title":"J. Insect Conserv."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Das, M., Liu, F.-L.C., Hartle, C., Yang, C.-C.S., and Chen, C.P.J. (2024). Ant Detective: An automated approach for counting ants in densely populated images and gaining insight into ant foraging behavior. arXiv.","DOI":"10.2139\/ssrn.5123383"},{"key":"ref_65","unstructured":"Serra Montes, P. (2024). Ant tracking\u2014Offline tracking system. Univ. Polit\u00e8cnica Catalunya Tech. Rep."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"IS24011","DOI":"10.1071\/IS24011","article-title":"Image-based recognition of parasitoid wasps using advanced neural networks","volume":"38","author":"Shirali","year":"2024","journal-title":"Invertebr. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s10343-022-00794-0","article-title":"Deep Learning Object Detection for Image Analysis of Cherry Fruit Fly (Rhagoletis cerasi L.) on Yellow Sticky Traps","volume":"75","author":"Salamut","year":"2023","journal-title":"Gesunde Pflanz."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1603\/AN13122","article-title":"Morphometric Differentiation of Fruit Fly Pest Species of the Anastrepha fraterculus Group (Diptera: Tephritidae)","volume":"107","author":"Perre","year":"2014","journal-title":"Ann. Entomol. Soc. Am."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C.E., Mann, H.M.R., and H\u00f8ye, T.T. (2023). Accurate detection and identification of insects from camera trap images with deep learning. PLOS Sustain. Transform., 2.","DOI":"10.1371\/journal.pstr.0000051"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhang, W., Huang, H., Sun, Y., and Wu, X. (2022). AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.1079384"},{"key":"ref_71","first-page":"e40","article-title":"Automatic counting and identification of two Drosophila melanogaster (Diptera: Drosophilidae) morphs with image-recognition artificial intelligence","volume":"156","author":"Robles","year":"2025","journal-title":"Can. Entomol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.aspen.2014.06.014","article-title":"Using Digital Image Processing for Counting Whiteflies in Soybean Leaves","volume":"17","year":"2014","journal-title":"J. Asia Pac. Entomol."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zhang, T., Li, K., Chen, X., Zhong, C., Luo, B., Grijalva, I., McCornack, B., Flippo, D., Sharda, A., and Wang, G. (2023). Aphid Cluster Recognition and Detection in the Wild Using Deep Learning Models. arXiv.","DOI":"10.1038\/s41598-023-38633-5"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1111\/syen.12543","article-title":"Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)","volume":"47","author":"Popkov","year":"2022","journal-title":"Syst. Entomol."},{"key":"ref_75","unstructured":"Popkov, A., Neimorovets, V.V., and Konstantinov, F.V. (Agric. For. Entomol., 2025). A web-based app for sunn pest species recognition (Hemiptera: Scutelleridae: Eurygaster) using machine learning, Agric. For. Entomol., advance online publication."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.compag.2016.02.003","article-title":"Automatic moth detection from trap images for pest management","volume":"123","author":"Ding","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_77","first-page":"47","article-title":"Lepidoptera classification using Convolutional Neural Network EfficientNet-B0","volume":"7","author":"Syamsudin","year":"2023","journal-title":"Indones. J. Artif. Intell. Data Min."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"20","DOI":"10.2478\/ijanmc-2023-0073","article-title":"Deep Learning Based Recognition of Lepidoptera Insects","volume":"8","author":"He","year":"2024","journal-title":"Int. J. Adv. Netw. Monit. Controls"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Shah, M., and Sinha, H. (2025, January 8). Automated image identification and classification of tiger beetles based on deep learning model. Proceedings of the IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC62904.2025.10903740"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.isprsjprs.2021.10.004","article-title":"Automatic large scale detection of red palm weevil infestation using street view images","volume":"182","author":"Kagan","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"365","DOI":"10.3844\/ajabssp.2011.365.376","article-title":"Red Palm Weevil (Rynchophorus Ferrugineous, Olivier) Recognition by Image Processing Techniques","volume":"6","author":"Hassan","year":"2011","journal-title":"Am. J. Agric. Biol. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Caruso, V., Shirali, H., Bouget, C., Cerretti, P., Curletti, G., de Groot, M., Groznik, E., Gutowski, J.M., Pyliatuk, C., and Roques, A. (2025). Image-based recognition using advanced neural networks can aid surveillance of Agrilus (Coleoptera, Buprestidae) jewel beetles. ARPHA Preprints.","DOI":"10.3897\/arphapreprints.e154842"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Lei, C., Lu, Y., Xing, Z., Zhang, J., Li, S., Wu, W., and Liu, S. (2024). A Honey Bee In-and-Out Counting Method Based on Multiple Object Tracking Algorithm. Insects, 15.","DOI":"10.3390\/insects15120974"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Ratnayake, A., Yasin, H., Naim, A., and Abas, P.E. (2023, January 23\u201325). Classification of Subspecies of Honey Bees using Convolutional Neural Network. Proceedings of the 2023 4th International Conference on Artificial Intelligence, Computational Intelligence and Its Applications (ACIIS), Bandar Seri Begawan, Brunei Darussalam.","DOI":"10.1109\/ACIIS59385.2023.10367282"},{"key":"ref_85","unstructured":"Karthiga, M., Sountharrajan, S., Nandhini, S.S., Suganya, E., and Sankarananth, S. (2021, January 25\u201327). A deep learning approach to classify the honeybee species and health identification. Proceedings of the 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Silva, M., Felisberto, B., Batista, M., Campos, A., Ribeiro, S., and Rabelo, R. (2023, January 24\u201326). An automatic ant counting and distribution estimation system using convolutional neural networks. Proceedings of the 25th International Conference on Enterprise Information Systems, Prague, Czech Republic.","DOI":"10.5220\/0011968900003467"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1111\/1755-0998.13567","article-title":"DiversityScanner: Robotic handling of small invertebrates with machine learning methods","volume":"22","author":"Pylatiuk","year":"2022","journal-title":"Mol. Ecol. Resour."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"bay081","DOI":"10.1093\/database\/bay081","article-title":"WaspBase: A genomic resource for the interactions among parasitic wasps, insect hosts and plants","volume":"2018","author":"Chen","year":"2018","journal-title":"Database"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Giering, S.L.C., Culverhouse, P.F., Johns, D.G., McQuatters-Gollop, A., and Pitois, S.G. (2022). Are Plankton Nets a Thing of the Past? An Assessment of in Situ Imaging of Zooplankton for Large-Scale Ecosystem Assessment and Policy Decision-Making. Front. Mar. Sci., 9.","DOI":"10.3389\/fmars.2022.986206"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1002\/lom3.10475","article-title":"The Underwater Vision Profiler 6: An Imaging Sensor of Particle Size Spectra and Plankton, for Autonomous and Cabled Platforms","volume":"20","author":"Picheral","year":"2022","journal-title":"Limnol. Oceanogr. Methods"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1146\/annurev-marine-041921-013023","article-title":"Machine Learning for the Study of Plankton and Marine Snow from Images","volume":"14","author":"Irisson","year":"2022","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Pollina, T., Larson, A.G., Lombard, F., Li, H., Le Guen, D., Colin, S., de Vargas, C., and Prakash, M. (2022). PlanktoScope: Affordable Modular Quantitative Imaging Platform for Citizen Oceanography. Front. Mar. Sci., 9.","DOI":"10.3389\/fmars.2022.949428"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Xu, X., Luo, W., Ren, Z., and Song, X. (2025). Intelligent Detection and Recognition of Marine Plankton by Digital Holography and Deep Learning. Sensors, 25.","DOI":"10.3390\/s25072325"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"e79760","DOI":"10.7554\/eLife.79760","article-title":"Microplankton Life Histories Revealed by Holographic Microscopy and Deep Learning","volume":"11","author":"Bachimanchi","year":"2022","journal-title":"eLife"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Zhang, W., Bi, H., Wang, D., Cheng, X., Cai, Z., and Ying, K. (2024). Automated Zooplankton Size Measurement Using Deep Learning: Overcoming the Limitations of Traditional Methods. Front. Mar. Sci., 11.","DOI":"10.3389\/fmars.2024.1341191"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Ciranni, M., Gjergji, A., Maracani, A., Murino, V., and Pastore, V.P. (March, January 28). In-Domain Self-Supervised Learning for Plankton Image Classification on a Budget. Proceedings of the 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Tucson, AZ, USA.","DOI":"10.1109\/WACVW65960.2025.00173"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1002\/lno.11672","article-title":"Trait-Based Approach Using in Situ Copepod Images Reveals Contrasting Ecological Patterns across an Arctic Ice Melt Zone","volume":"66","author":"Vilgrain","year":"2021","journal-title":"Limnol. Oceanogr."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1002\/lno.12101","article-title":"Machine Learning Techniques to Characterize Functional Traits of Plankton from Image Data","volume":"67","author":"Orenstein","year":"2022","journal-title":"Limnol. Oceanogr."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"102989","DOI":"10.1016\/j.ecoinf.2024.102989","article-title":"Surveying the Deep: A Review of Computer Vision in the Benthos","volume":"86","author":"Trotter","year":"2025","journal-title":"Ecol. Inform."},{"key":"ref_100","unstructured":"Picheral, M., Colin, S., and Irisson, J.-O. (EcoTaxa, 2022). EcoTaxa, A Tool for the Taxonomic Classification of Images."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"15914","DOI":"10.1038\/s41598-022-19939-2","article-title":"FathomNet: A Global Image Database for Enabling Artificial Intelligence in the Ocean","volume":"12","author":"Katija","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1038\/s41597-025-04491-1","article-title":"BenthicNet: A Global Compilation of Seafloor Images for Deep Learning Applications","volume":"12","author":"Lowe","year":"2025","journal-title":"Sci. Data"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Azmi, E., Alibabaei, K., Kozlov, V., L\u00f3pez Garc\u00eda, \u00c1., Schaap, D., and Sipos, G. (2025, January 16\u201318). iMagine: AI-Powered Image Data Analysis in Aquatic Science. Proceedings of the Platform for Advanced Scientific Computing Conference, Brugg, Switzerland.","DOI":"10.1145\/3732775.3733584"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"231678","DOI":"10.1098\/rsos.231678","article-title":"New Interactive Machine Learning Tool for Marine Image Analysis","volume":"11","author":"Clark","year":"2024","journal-title":"R. Soc. Open Sci."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1002\/lol2.10392","article-title":"Deep-Learning-Powered Data Analysis in Plankton Ecology","volume":"9","author":"Bachimanchi","year":"2024","journal-title":"Limnol. Oceanogr. Lett."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"17188","DOI":"10.1038\/s41598-020-74215-5","article-title":"Understanding deep learning in land use classification based on Sentinel-2 time series","volume":"10","author":"Atzberger","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1038\/s41559-023-02206-6","article-title":"A high-resolution canopy height model of the Earth","volume":"7","author":"Lang","year":"2023","journal-title":"Nat. Ecol. Evol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"113195","DOI":"10.1016\/j.rse.2022.113195","article-title":"Fifty years of Landsat science and impacts","volume":"280","author":"Wulder","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"111758","DOI":"10.1016\/j.rse.2020.111758","article-title":"Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions","volume":"242","author":"Berger","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"5601413","DOI":"10.1109\/TGRS.2021.3050551","article-title":"A Flexible Reference-Insensitive Spatiotemporal Fusion Model for Remote Sensing Images Using Conditional Generative Adversarial Network","volume":"60","author":"Tan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s10712-018-9478-y","article-title":"Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods","volume":"40","author":"Verrelst","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"17656","DOI":"10.1038\/s41598-019-53797-9","article-title":"Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery","volume":"9","author":"Kattenborn","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_115","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3D classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.srs.2020.100002","article-title":"The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth\u2019s forests and topography","volume":"1","author":"Dubayah","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 11\u201316). Global land use\/land cover with Sentinel 2 and deep learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1111\/2041-210X.13472","article-title":"DeepForest: A Python package for RGB deep learning tree crown delineation","volume":"11","author":"Weinstein","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"18590","DOI":"10.1038\/s41598-022-21910-0","article-title":"Ensembles of data-efficient vision transformers as a new paradigm for automated classification in ecology","volume":"12","author":"Kyathanahally","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_121","first-page":"269","article-title":"Classification and change-detection using Landsat TM data: When and how to correct atmospheric effects?","volume":"89","author":"Song","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1111\/gcb.13388","article-title":"Allometric equations for integrating remote sensing imagery into forest monitoring programmes","volume":"23","author":"Jucker","year":"2017","journal-title":"Glob. Change Biol."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Xu, X., Yang, J., Qi, S., Ma, Y., Liu, W., Li, L., Zhang, H., and Liu, Y. (2024). Estimation of Forest Aboveground Biomass Using Sentinel-1\/2 Synergized with Extrapolated Parameters from LiDAR Data and Analysis of Its Ecological Driving Factors. Remote Sens., 17.","DOI":"10.3390\/rs17142358"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Bergm\u00fcller, K.O., and Vanderwel, M.C. (2022). Predicting tree mortality using spectral indices derived from multispectral UAV imagery. Remote Sens., 14.","DOI":"10.3390\/rs14092195"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Qin, B., Sun, F., Shen, W., Dong, B., Ma, S., Huo, X., and Lan, P. (2023). Deep learning-based pine nematode trees\u2019 identification using multispectral and visible UAV imagery. Drones, 7.","DOI":"10.3390\/drones7030183"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"e10559","DOI":"10.1002\/ece3.10559","article-title":"Trends in the application of remote sensing in blue carbon science","volume":"13","author":"Wartman","year":"2023","journal-title":"Ecol. Evol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"104501","DOI":"10.1016\/j.earscirev.2023.104501","article-title":"Advances in Earth observation and machine learning for quantifying blue carbon","volume":"243","author":"Pham","year":"2023","journal-title":"Earth-Sci. Rev."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"109621","DOI":"10.1016\/j.dib.2023.109621","article-title":"Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning","volume":"50","author":"Maxwell","year":"2023","journal-title":"Data Brief"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"6824","DOI":"10.1080\/01431161.2022.2145584","article-title":"The current status, potential and challenges of remote sensing for large-scale mangrove studies","volume":"43","author":"Lu","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13007-019-0479-8","article-title":"Plant disease identification using explainable 3D deep learning on hyperspectral images","volume":"15","author":"Nagasubramanian","year":"2019","journal-title":"Plant Methods"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Kim, S.R., Lee, W.K., Lim, C.H., Kim, M., Kafatos, M.C., Lee, S.H., and Lee, S.S. (2018). Hyperspectral analysis of pine wilt disease to determine an optimal detection index. Forests, 9.","DOI":"10.3390\/f9030115"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.rse.2013.05.029","article-title":"A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products","volume":"136","author":"Yebra","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Marino, E., Yebra, M., Guill\u00e9n-Climent, M., Algeet, N., Tom\u00e9, J.L., Madrigal, J., Guijarro, M., and Hernando, C. (2020). Investigating live fuel moisture content estimation in fire-prone shrubland from remote sensing using empirical modelling and RTM simulations. Remote Sens., 12.","DOI":"10.3390\/rs12142251"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Cunill Camprubi, A., Gonzalez-Moreno, P., and Resco de Dios, V. (2022). Live fuel moisture content mapping in the Mediterranean Basin using random forests and combining MODIS spectral and thermal data. Remote Sens., 14.","DOI":"10.3390\/rs14133162"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1071\/WF22188","article-title":"Projecting live fuel moisture content via deep learning","volume":"32","author":"Miller","year":"2023","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s40808-025-02561-2","article-title":"A hybrid framework to estimate live fuel moisture content through land surface modelling","volume":"11","author":"Santos","year":"2025","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1126\/science.1118051","article-title":"Selective logging in the Brazilian Amazon","volume":"310","author":"Asner","year":"2005","journal-title":"Science"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.rse.2017.10.034","article-title":"Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2","volume":"204","author":"Reiche","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"112411","DOI":"10.1016\/j.rse.2021.112411","article-title":"Detecting tropical selective logging with C-band SAR data may require a time series approach","volume":"259","author":"Hethcoat","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1002\/ldr.644","article-title":"Soil carbon sequestration in sub-Saharan Africa: A review","volume":"16","author":"Lal","year":"2005","journal-title":"Land Degrad. Dev."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). \u201cWhy Should I Trust You?\u201d: Explaining the Predictions of Any Classifier. KDD \u201916, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/88\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T13:04:31Z","timestamp":1771419871000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/88"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,18]]},"references-count":141,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["jimaging12020088"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12020088","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,18]]}}}