{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T22:40:13Z","timestamp":1791499213795,"version":"4.3.4"},"reference-count":60,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T00:00:00Z","timestamp":1684713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62265015"],"award-info":[{"award-number":["62265015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cotton is one of the most important cash crops in Xinjiang, and timely seedling inspection and replenishment at the seedling stage are essential for cotton\u2019s late production management and yield formation. The background conditions of the cotton seedling stage are complex and variable, and deep learning methods are widely used to extract target objects from the complex background. Therefore, this study takes seedling cotton as the research object and uses three deep learning algorithms, YOLOv5, YOLOv7, and CenterNet, for cotton seedling detection and counting using images at six different times of the cotton seedling period based on multispectral images collected by UAVs to develop a model applicable to the whole cotton seedling period. The results showed that when tested with data collected at different times, YOLOv7 performed better overall in detection and counting, and the T4 dataset performed better in each test set. Precision, Recall, and F1-Score values with the best test results were 96.9%, 96.6%, and 96.7%, respectively, and the R2, RMSE, and RRMSE indexes were 0.94, 3.83, and 2.72%, respectively. In conclusion, the UAV multispectral images acquired about 23 days after cotton sowing (T4) with the YOLOv7 algorithm achieved rapid and accurate seedling detection and counting throughout the cotton seedling stage.<\/jats:p>","DOI":"10.3390\/rs15102680","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T02:28:42Z","timestamp":1684722522000},"page":"2680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods"],"prefix":"10.3390","volume":"15","author":[{"given":"Yingxiang","family":"Feng","sequence":"first","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiru","family":"Ma","sequence":"additional","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-8865","authenticated-orcid":false,"given":"Ze","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7781-3005","authenticated-orcid":false,"given":"Pan","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Lv","sequence":"additional","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mavridou, E., Vrochidou, E., Papakostas, G.A., Pachidis, T., and Kaburlasos, V.G. (2019). Machine Vision Systems in Precision Agriculture for Crop Farming. J. Imaging, 5.","DOI":"10.3390\/jimaging5120089"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104976","DOI":"10.1016\/j.compag.2019.104976","article-title":"Image Processing Algorithms for Infield Single Cotton Boll Counting and Yield Prediction","volume":"166","author":"Sun","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kitano, B.T., Mendes, C.C.T., Geus, A.R., Oliveira, H.C., and Souza, J.R. (2019). Corn Plant Counting Using Deep Learning and UAV Images. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2019.2930549"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1109\/LGRS.2018.2819944","article-title":"Failure Detection in Row Crops from UAV Images Using Morphological Operators","volume":"15","author":"Oliveira","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.rse.2014.06.006","article-title":"Green Area Index from an Unmanned Aerial System over Wheat and Rapeseed Crops","volume":"152","author":"Verger","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"48572","DOI":"10.1109\/ACCESS.2019.2909530","article-title":"Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges","volume":"7","author":"Shakhatreh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.biosystemseng.2020.10.013","article-title":"Mapping Crop Stand Count and Planting Uniformity Using High Resolution Imagery in a Maize Crop","volume":"200","author":"Shirzadifar","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106214","DOI":"10.1016\/j.compag.2021.106214","article-title":"Early Corn Stand Count of Different Cropping Systems Using UAV-Imagery and Deep Learning","volume":"186","author":"Vong","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lin, Z., and Guo, W. (2021). Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models. Remote Sens., 13.","DOI":"10.3390\/rs13142822"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107339","DOI":"10.1016\/j.compag.2022.107339","article-title":"Multi-Object Tracking Using Deep SORT and Modified CenterNet in Cotton Seedling Counting","volume":"202","author":"Yang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1186\/s13007-019-0510-0","article-title":"Image Analysis-Based Recognition and Quantification of Grain Number per Panicle in Rice","volume":"15","author":"Wu","year":"2019","journal-title":"Plant Methods"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wu, J., Yang, G., Yang, X., Xu, B., Han, L., and Zhu, Y. (2019). Automatic Counting of in Situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11060691"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13007-019-0399-7","article-title":"The Estimation of Crop Emergence in Potatoes by UAV RGB Imagery","volume":"15","author":"Li","year":"2019","journal-title":"Plant Methods"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Schirrmann, M., Giebel, A., Gleiniger, F., Pflanz, M., Lentschke, J., and Dammer, K.-H. (2016). Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8090706"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Maresma, \u00c1., Ariza, M., Mart\u00ednez, E., Lloveras, J., and Mart\u00ednez-Casasnovas, J.A. (2016). Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea Mays L.) from a Standard UAV Service. Remote Sens., 8.","DOI":"10.3390\/rs8120973"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1007\/s11119-022-09907-1","article-title":"A Fast and Robust Method for Plant Count in Sunflower and Maize at Different Seedling Stages Using High-Resolution UAV RGB Imagery","volume":"23","author":"Bai","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s13007-015-0056-8","article-title":"Plant Phenotyping: From Bean Weighing to Image Analysis","volume":"11","author":"Walter","year":"2015","journal-title":"Plant Methods"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105711","DOI":"10.1016\/j.compag.2020.105711","article-title":"Evaluation of Cotton Emergence Using UAV-Based Imagery and Deep Learning","volume":"177","author":"Feng","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","first-page":"100312","article-title":"Reflectance Calibration of UAV-Based Visible and near-Infrared Digital Images Acquired under Variant Altitude and Illumination Conditions","volume":"18","author":"Nanni","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Senecal, J.J., Sheppard, J.W., and Shaw, J.A. (2019, January 14\u201319). Efficient Convolutional Neural Networks for Multi-Spectral Image Classification. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851840"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Su, H., Jung, C., and Yu, L. (2021). Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis. Sensors, 21.","DOI":"10.3390\/s21113610"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"534853","DOI":"10.3389\/fpls.2020.534853","article-title":"Sorghum Panicle Detection and Counting Using Unmanned Aerial System Images and Deep Learning","volume":"11","author":"Lin","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Banerjee, B.P., Spangenberg, G., and Kant, S. (2020). Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation. Remote Sens., 12.","DOI":"10.3390\/rs12193164"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113198","DOI":"10.1016\/j.rse.2022.113198","article-title":"Multi-Sensor Spectral Synergies for Crop Stress Detection and Monitoring in the Optical Domain: A Review","volume":"280","author":"Berger","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2019.12.010","article-title":"A Convolutional Neural Network Approach for Counting and Geolocating Citrus-Trees in UAV Multispectral Imagery","volume":"160","author":"Osco","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lee, H., Wang, J., and Leblon, B. (2020). Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn. Remote Sens., 12.","DOI":"10.3390\/rs12132071"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.compag.2011.11.007","article-title":"Determination of the Number of Green Apples in RGB Images Recorded in Orchards","volume":"81","author":"Linker","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s11517-006-0044-2","article-title":"Automated Image Processing Method for the Diagnosis and Classification of Malaria on Thin Blood Smears","volume":"44","author":"Ross","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2017.06.007","article-title":"Estimates of Plant Density of Wheat Crops at Emergence from Very Low Altitude UAV Imagery","volume":"198","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105223","DOI":"10.1016\/j.compag.2020.105223","article-title":"Early Season Detection of Rice Plants Using RGB, NIR-G-B and Multispectral Images from Unmanned Aerial Vehicle (UAV)","volume":"169","author":"Zheng","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10261","DOI":"10.1109\/TPAMI.2021.3134684","article-title":"MobileSal: Extremely Efficient RGB-D Salient Object Detection","volume":"44","author":"Wu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep Learning in Agriculture: A Survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/JSTARS.2018.2793849","article-title":"Automatic Tobacco Plant Detection in UAV Images via Deep Neural Networks","volume":"11","author":"Fan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"106493","DOI":"10.1016\/j.compag.2021.106493","article-title":"Automatic UAV-Based Counting of Seedlings in Sugar-Beet Field and Extension to Maize and Strawberry","volume":"191","author":"Barreto","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107008","DOI":"10.1016\/j.compag.2022.107008","article-title":"Corn Emergence Uniformity Estimation and Mapping Using UAV Imagery and Deep Learning","volume":"198","author":"Vong","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ahmad, I., Yang, Y., Yue, Y., Ye, C., Hassan, M., Cheng, X., Wu, Y., and Zhang, Y. (2022). Deep Learning Based Detector YOLOv5 for Identifying Insect Pests. Appl. Sci., 12.","DOI":"10.3390\/app121910167"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106780","DOI":"10.1016\/j.compag.2022.106780","article-title":"An Improved YOLOv5 Model Based on Visual Attention Mechanism: Application to Recognition of Tomato Virus Disease","volume":"194","author":"Qi","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yan, B., Fan, P., Lei, X., Liu, Z., and Yang, F. (2021). A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. Remote Sens., 13.","DOI":"10.3390\/rs13091619"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jiang, K., Xie, T., Yan, R., Wen, X., Li, D., Jiang, H., Jiang, N., Feng, L., Duan, X., and Wang, J. (2022). An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation. Agriculture, 12.","DOI":"10.3390\/agriculture12101659"},{"key":"ref_41","unstructured":"Xia, Y., Nguyen, M., and Yan, W.Q. (2022). Revised Selected Papers, Proceedings of the Image and Vision Computing: 37th International Conference, IVCNZ 2022, Auckland, New Zealand, 24\u201325 November 2022, Springer Nature."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yang, Z., Ni, C., Li, L., Luo, W., and Qin, Y. (2022). Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques. Sensors, 22.","DOI":"10.3390\/s22218459"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s42421-020-00025-w","article-title":"Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis","volume":"2","author":"Mandal","year":"2020","journal-title":"J. Big Data Anal. Transp."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, H., Yuan, G., Yang, L., Liu, K., and Zhou, H. (2022). An Appearance Defect Detection Method for Cigarettes Based on C-CenterNet. Electronics, 11.","DOI":"10.3390\/electronics11142182"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1002\/ps.6656","article-title":"Evaluation of Different Deep Convolutional Neural Networks for Detection of Broadleaf Weed Seedlings in Wheat","volume":"78","author":"Zhuang","year":"2022","journal-title":"Pest Manag. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhao, L., and Li, S. (2020). Object Detection Algorithm Based on Improved YOLOv3. Electronics, 9.","DOI":"10.3390\/electronics9030537"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1109\/JSTARS.2022.3140776","article-title":"Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-V4","volume":"15","author":"Zakria","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wu, W., Liu, H., Li, L., Long, Y., Wang, X., Wang, Z., Li, J., and 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.","DOI":"10.1371\/journal.pone.0259283"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gallo, I., Rehman, A.U., Dehkordi, R.H., Landro, N., La Grassa, R., and Boschetti, M. (2023). Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset from UAV Images. Remote Sens., 15.","DOI":"10.3390\/rs15020539"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, C., Liu, P., Wang, H., and Jin, Y. (2022, January 17\u201322). Saliency-Based Centernet for Ship Detection in SAR Images. Proceedings of the IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883396"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Khaki, S., Pham, H., Khalilzadeh, Z., Masoud, A., Safaei, N., Han, Y., Kent, W., and Wang, L. (2022). High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0268762"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Feng, A., Zhou, J., Vories, E., and Sudduth, K.A. (2020). Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral Imagery with Customized Image Alignment and Stitching Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12111764"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"He, J., Zhang, N., Su, X., Lu, J., Yao, X., Cheng, T., Zhu, Y., Cao, W., and Tian, Y. (2019). Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles. Remote Sens., 11.","DOI":"10.3390\/rs11151809"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Suo, C., McGovern, E., and Gilmer, A. (2019). Coastal Dune Vegetation Mapping Using a Multispectral Sensor Mounted on an UAS. Remote Sens., 11.","DOI":"10.3390\/rs11151814"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Xia, L., Zhang, R., Chen, L., Huang, Y., Xu, G., Wen, Y., and Yi, T. (2019). Monitor Cotton Budding Using SVM and UAV Images. Appl. Sci., 9.","DOI":"10.3390\/app9204312"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., and Guo, X. (2022). Real-Time Vehicle Detection Based on Improved YOLO V5. Sustainability, 14.","DOI":"10.3390\/su141912274"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tang, Y., Zou, X., Wu, M., Tang, W., Meng, F., Zhang, Y., and Kang, H. (2022). Adaptive Active Positioning of Camellia Oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm. Appl. Sci., 12.","DOI":"10.3390\/app122412959"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"An, K., Chen, Y., Wang, S., and Xiao, Z. (2021). RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving. Appl. Sci., 11.","DOI":"10.3390\/app11125621"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Krichen, M., Mihoub, A., Alzahrani, M.Y., Adoni, W.Y.H., and Nahhal, T. (2022, January 9\u201311). Are Formal Methods Applicable To Machine Learning And Artificial Intelligence?. Proceedings of the 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia.","DOI":"10.1109\/SMARTTECH54121.2022.00025"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1145\/3503914","article-title":"Toward Verified Artificial Intelligence","volume":"65","author":"Seshia","year":"2022","journal-title":"Commun. ACM"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2680\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:39:39Z","timestamp":1760125179000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,22]]},"references-count":60,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102680"],"URL":"https:\/\/doi.org\/10.3390\/rs15102680","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,22]]}}}