{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T14:47:36Z","timestamp":1784386056380,"version":"3.55.0"},"reference-count":71,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T00:00:00Z","timestamp":1608854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["433783\/2018-4; 313887\/2018-7; 303559\/2019-5; 304052\/2019-1"],"award-info":[{"award-number":["433783\/2018-4; 313887\/2018-7; 303559\/2019-5; 304052\/2019-1"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["CAPES; Finance Code 001"],"award-info":[{"award-number":["CAPES; Finance Code 001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005667","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e Inova\u00e7\u00e3o do Estado de Santa Catarina","doi-asserted-by":"publisher","award":["FAPESC 2017TR1762; 2019TR816"],"award-info":[{"award-number":["FAPESC 2017TR1762; 2019TR816"]}],"id":[{"id":"10.13039\/501100005667","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. Specifically, in fruit detection problems, several recent works were developed using Deep Learning (DL) methods applied in images acquired in different acquisition levels. However, the increasing use of anti-hail plastic net cover in commercial orchards highlights the importance of terrestrial remote sensing systems. Apples are one of the most highly-challenging fruits to be detected in images, mainly because of the target occlusion problem occurrence. Additionally, the introduction of high-density apple tree orchards makes the identification of single fruits a real challenge. To support farmers to detect apple fruits efficiently, this paper presents an approach based on the Adaptive Training Sample Selection (ATSS) deep learning method applied to close-range and low-cost terrestrial RGB images. The correct identification supports apple production forecasting and gives local producers a better idea of forthcoming management practices. The main advantage of the ATSS method is that only the center point of the objects is labeled, which is much more practicable and realistic than bounding-box annotations in heavily dense fruit orchards. Additionally, we evaluated other object detection methods such as RetinaNet, Libra Regions with Convolutional Neural Network (R-CNN), Cascade R-CNN, Faster R-CNN, Feature Selective Anchor-Free (FSAF), and High-Resolution Network (HRNet). The study area is a highly-dense apple orchard consisting of Fuji Suprema apple fruits (Malus domestica Borkh) located in a smallholder farm in the state of Santa Catarina (southern Brazil). A total of 398 terrestrial images were taken nearly perpendicularly in front of the trees by a professional camera, assuring both a good vertical coverage of the apple trees in terms of heights and overlapping between picture frames. After, the high-resolution RGB images were divided into several patches for helping the detection of small and\/or occluded apples. A total of 3119, 840, and 2010 patches were used for training, validation, and testing, respectively. Moreover, the proposed method\u2019s generalization capability was assessed by applying simulated image corruptions to the test set images with different severity levels, including noise, blurs, weather, and digital processing. Experiments were also conducted by varying the bounding box size (80, 100, 120, 140, 160, and 180 pixels) in the image original for the proposed approach. Our results showed that the ATSS-based method slightly outperformed all other deep learning methods, between 2.4% and 0.3%. Also, we verified that the best result was obtained with a bounding box size of 160 \u00d7 160 pixels. The proposed method was robust regarding most of the corruption, except for snow, frost, and fog weather conditions. Finally, a benchmark of the reported dataset is also generated and publicly available.<\/jats:p>","DOI":"10.3390\/rs13010054","type":"journal-article","created":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T09:30:19Z","timestamp":1608888619000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["ATSS Deep Learning-Based Approach to Detect Apple Fruits"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3334-4507","authenticated-orcid":false,"given":"Leonardo Joso\u00e9","family":"Biffi","sequence":"first","affiliation":[{"name":"Department of Environmental and Sanitation Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages 88520-000, SC, Brazil"},{"name":"Graduate Program in Geodetic Sciences, Federal University of Paran\u00e1 (UFPR), Avenida Coronel Francisco Her\u00e1clito dos Santos 210, Curitiba 81531-990, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1717-7657","authenticated-orcid":false,"given":"Edson","family":"Mitishita","sequence":"additional","affiliation":[{"name":"Graduate Program in Geodetic Sciences, Federal University of Paran\u00e1 (UFPR), Avenida Coronel Francisco Her\u00e1clito dos Santos 210, Curitiba 81531-990, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0564-7818","authenticated-orcid":false,"given":"Veraldo","family":"Liesenberg","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages 88520-000, SC, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6968-623X","authenticated-orcid":false,"given":"Anderson Aparecido dos","family":"Santos","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Federal University of Mato Grosso do Sul (UFMS), Cidade Universit\u00e1ria, Av. Costa e Silva-Pioneiros, Campo Grande 79070-900, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4527-5724","authenticated-orcid":false,"given":"Diogo Nunes","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Federal University of Mato Grosso do Sul (UFMS), Cidade Universit\u00e1ria, Av. Costa e Silva-Pioneiros, Campo Grande 79070-900, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5249-3893","authenticated-orcid":false,"given":"Nayara Vasconcelos","family":"Estrabis","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul (UFMS), Cidade Universit\u00e1ria, Av. Costa e Silva-Pioneiros, Campo Grande 79070-900, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8274-2707","authenticated-orcid":false,"given":"Jonathan de Andrade","family":"Silva","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Federal University of Mato Grosso do Sul (UFMS), Cidade Universit\u00e1ria, Av. Costa e Silva-Pioneiros, Campo Grande 79070-900, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0258-536X","authenticated-orcid":false,"given":"Lucas Prado","family":"Osco","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Architecture and Urbanism, University of Western S\u00e3o Paulo (UNOESTE), Rodovia Raposo Tavares, km 572\u2014Limoeiro, Pres. Prudente 19067-175, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6633-2903","authenticated-orcid":false,"given":"Ana Paula Marques","family":"Ramos","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Architecture and Urbanism, University of Western S\u00e3o Paulo (UNOESTE), Rodovia Raposo Tavares, km 572\u2014Limoeiro, Pres. Prudente 19067-175, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2669-7147","authenticated-orcid":false,"given":"Jorge Antonio Silva","family":"Centeno","sequence":"additional","affiliation":[{"name":"Graduate Program in Geodetic Sciences, Federal University of Paran\u00e1 (UFPR), Avenida Coronel Francisco Her\u00e1clito dos Santos 210, Curitiba 81531-990, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7401-3881","authenticated-orcid":false,"given":"Marcos Benedito","family":"Schimalski","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages 88520-000, SC, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9545-7035","authenticated-orcid":false,"given":"Leo","family":"Rufato","sequence":"additional","affiliation":[{"name":"Department of Agronomy, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages 88520-000, SC, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6640-1961","authenticated-orcid":false,"given":"S\u00edlvio Lu\u00eds Rafaeli","family":"Neto","sequence":"additional","affiliation":[{"name":"Department of Environmental and Sanitation Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages 88520-000, SC, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9096-6866","authenticated-orcid":false,"given":"Jos\u00e9","family":"Marcato Junior","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul (UFMS), Cidade Universit\u00e1ria, Av. Costa e Silva-Pioneiros, Campo Grande 79070-900, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8815-6653","authenticated-orcid":false,"given":"Wesley Nunes","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Federal University of Mato Grosso do Sul (UFMS), Cidade Universit\u00e1ria, Av. Costa e Silva-Pioneiros, Campo Grande 79070-900, MS, Brazil"},{"name":"Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul (UFMS), Cidade Universit\u00e1ria, Av. Costa e Silva-Pioneiros, Campo Grande 79070-900, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dian Bah, M., Hafiane, A., and Canals, R. (2018). Deep learning with unsupervised data labeling for weed detection in line crops in UAV images. Remote Sens., 10.","DOI":"10.20944\/preprints201809.0088.v1"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1007\/s11119-020-09709-3","article-title":"Passion fruit detection and counting based on multiple scale faster R-CNN using RGB-D images","volume":"21","author":"Tu","year":"2020","journal-title":"Precision Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.compag.2018.12.003","article-title":"Estimation of nitrogen and carbon content from soybean leaf reflectance spectra using wavelet analysis under shade stress","volume":"156","author":"Chen","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-018-0366-8","article-title":"Detection and analysis of wheat spikes using Convolutional Neural Networks","volume":"14","author":"Hasan","year":"2018","journal-title":"Plant Methods"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111410","DOI":"10.1016\/j.rse.2019.111410","article-title":"High resolution wheat yield mapping using Sentinel-2","volume":"233","author":"Hunt","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Salam\u00ed, E., Gallardo, A., Skorobogatov, G., and Barrado, C. (2019). On-the-fly olive tree counting using a UAS and cloud services. Remote Sens., 11.","DOI":"10.3390\/rs11030316"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.11.042609","article-title":"Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community","volume":"11","author":"Ball","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2018.09.008","article-title":"UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras","volume":"146","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Meng, L., Peng, Z., Zhou, J., Zhang, J., Lu, Z., Baumann, A., and Du, Y. (2020). Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety. Remote Sens., 12.","DOI":"10.3390\/rs12010182"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Han, L., and Zhu, L. (2020). How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?. Remote Sens., 12.","DOI":"10.3390\/rs12030417"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.patrec.2020.02.006","article-title":"CMIR-NET: A deep learning based model for cross-modal retrieval in remote sensing","volume":"131","author":"Chaudhuri","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Lobo Torres, D., Queiroz Feitosa, R., Nigri Happ, P., Elena Cu\u00e9 La Rosa, L., Marcato Junior, J., Martins, J., Ol\u00e3 Bressan, P., Gon\u00e7alves, W.N., and Liesenberg, V. (2020). Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery. Sensors, 20.","DOI":"10.3390\/s20020563"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, L., Huang, L., Fan, L., Huang, J., Huang, F., Chen, J., Zhang, Z., and Wang, Y. (2020). Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network. Sensors, 20.","DOI":"10.3390\/s20061576"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Castro, W., Marcato Junior, J., Polidoro, C., Osco, L.P., Gon\u00e7alves, W., Rodrigues, L., Santos, M., Jank, L., Barrios, S., and Valle, C. (2020). Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery. Sensors, 20.","DOI":"10.3390\/s20174802"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/exsy.12400","article-title":"A systematic review on deep learning architectures and applications","volume":"36","author":"Khamparia","year":"2019","journal-title":"Expert Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"126030","DOI":"10.1016\/j.eja.2020.126030","article-title":"Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV","volume":"115","author":"Egea","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.3389\/fpls.2020.01086","article-title":"A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique","volume":"11","author":"Valente","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Veeranampalayam Sivakumar, A.N., Li, J., Scott, S., Psota, E.J., Jhala, A., Luck, J.D., and Shi, Y. (2020). Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12132136"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, W., Fu, H., Yu, L., and Cracknell, A. (2016). Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images. Remote Sens., 9.","DOI":"10.3390\/rs9010022"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Csillik, O., Cherbini, J., Johnson, R., Lyons, A., and Kelly, M. (2018). Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks. Drones, 2.","DOI":"10.3390\/drones2040039"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.eaef.2018.03.001","article-title":"Detecting greenhouse strawberries (mature and immature), using deep convolutional neural network","volume":"11","author":"Habaragamuwa","year":"2018","journal-title":"Eng. Agric. Environ. Food"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kirk, R., Cielniak, G., and Mangan, M. (2020). L*a*b*Fruits: A rapid and robust outdoor fruit detection system combining bio-inspired features with one-stage deep learning networks. Sensors, 20.","DOI":"10.3390\/s20010275"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, X., Chen, S.W., Aditya, S., Sivakumar, N., Dcunha, S., Qu, C., Taylor, C.J., Das, J., and Kumar, V. (2018, January 1\u20135). Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594239"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1002\/rob.21699","article-title":"Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards","volume":"34","author":"Bargoti","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.engappai.2018.09.011","article-title":"MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard","volume":"77","author":"Kestur","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning\u2014Method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.compind.2018.03.010","article-title":"Apple flower detection using deep convolutional networks","volume":"99","author":"Dias","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105742","DOI":"10.1016\/j.compag.2020.105742","article-title":"Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments","volume":"178","author":"Wu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"59069","DOI":"10.1109\/ACCESS.2019.2914929","article-title":"Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks","volume":"7","author":"Jiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"26911","DOI":"10.1109\/ACCESS.2020.2971524","article-title":"Deep Learning Approach for Apple Edge Detection to Remotely Monitor Apple Growth in Orchards","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","article-title":"Apple detection during different growth stages in orchards using the improved YOLO-V3 model","volume":"157","author":"Tian","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"105108","DOI":"10.1016\/j.compag.2019.105108","article-title":"Fast implementation of real-time fruit detection in apple orchards using deep learning","volume":"168","author":"Kang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.compag.2019.05.016","article-title":"Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities","volume":"162","author":"Vilaplana","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105634","DOI":"10.1016\/j.compag.2020.105634","article-title":"Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN","volume":"176","author":"Gao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_41","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017). Focal Loss for Dense Object Detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13\u201319). Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition CVPR, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Srivastava, L.M. (2002). CHAPTER 17\u2014Fruit Development and Ripening. Plant Growth and Development, Academic Press.","DOI":"10.1016\/B978-012660570-9\/50159-3"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.scienta.2019.04.079","article-title":"Effect of hail nets and fertilization management on the nutritional status, growth and production of apple trees","volume":"255","year":"2019","journal-title":"Sci. Hortic."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"386","DOI":"10.5513\/JCEA01\/21.2.2582","article-title":"The influence of differently coloured anti-hail nets and geomorphologic characteristics on microclimatic and light conditions in apple orchards","volume":"21","author":"Tojnko","year":"2020","journal-title":"J. Cent. Eur. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1590\/1678-4499.2016459","article-title":"Microclimate alterations caused by agricultural hail net coverage and effects on apple tree yield in subtropical climate of Southern Brazil","volume":"77","author":"Bosco","year":"2018","journal-title":"Bragantia"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bosco, L.C., Bergamaschi, H., and Marodin, G.A. (2020). Solar radiation effects on growth, anatomy, and physiology of apple trees in a temperate climate of Brazil. Int. J. Biometeorol., 1969\u20131980.","DOI":"10.1007\/s00484-020-01987-w"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 15\u201320). Libra R-CNN: Towards Balanced Learning for Object Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhu, C., He, Y., and Savvides, M. (2019, January 15\u201320). Feature Selective Anchor-Free Module for Single-Shot Object Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00093"},{"key":"ref_52","unstructured":"Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., and Wang, X. (2020). Deep High-Resolution Representation Learning for Visual Recognition. IEEE Trans. Pattern Anal. Mach. Intell., 1."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1127\/0941-2948\/2013\/0507","article-title":"K\u00f6ppen\u2019s climate classification map for Brazil","volume":"22","author":"Alvares","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_54","unstructured":"Soil Survey Staff (1999). Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys, Natural Resources Conservation Service, USDA. [2nd ed.]."},{"key":"ref_55","unstructured":"dos Santos, H.G., Jacomine, P.K.T., Dos Anjos, L., De Oliveira, V., Lumbreras, J.F., Coelho, M.R., De Almeida, J., de Araujo Filho, J., De Oliveira, J., and Cunha, T.J.F. (2018). Sistema Brasileiro de Classifica\u00e7\u00e3o de Solos, Embrapa."},{"key":"ref_56","unstructured":"National Water Agency (ANA) (2020, November 02). HIDROWEB V3.1.1\u2014S\u00e9ries Hist\u00f3ricas de Esta\u00e7\u00f5es, Available online: http:\/\/www.snirh.gov.br\/hidroweb\/serieshistoricas."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1590\/S0034-76122011000400013","article-title":"A cadeia produtiva da ma\u00e7\u00e3 em Santa Catarina: Competitividade segundo produ\u00e7\u00e3o e packing house","volume":"45","author":"Bittencourt","year":"2011","journal-title":"Rev. Admin. P\u00fablica"},{"key":"ref_58","unstructured":"Brazilian Institute of Geography and Statistics (IBGE) (2019). Censo Agropecu\u00e1rio 2017: Resultados Definitivos, IBGE."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1590\/1984-70332019v19n3p47","article-title":"A brief history of the forty-five years of the Epagri apple breeding program in Brazil","volume":"19","author":"Denardi","year":"2019","journal-title":"Crop. Breed. Appl. Biotechnol."},{"key":"ref_60","unstructured":"Brazilian Institute of Geography and Statistics (IBGE) (2020, April 20). Geosciences: Continuos Catographic Bases, Available online: https:\/\/www.ibge.gov.br\/geociencias\/cartas-e-mapas\/bases-cartograficas-continuas\/15807-estados.html?=&t=sobre."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"6587","DOI":"10.3390\/rs6076587","article-title":"The use of a hand-held camera for individual tree 3D mapping in forest sample plots","volume":"6","author":"Liang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_62","first-page":"48","article-title":"405-Fuji Suprema: Nova cultivar de macieira","volume":"10","author":"Petri","year":"1997","journal-title":"Agropecu. Catarin. Florian\u00f3polis"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Dutta, A., and Zisserman, A. (2019, January 21\u201325). The VIA Annotation Software for Images, Audio and Video. Proceedings of the 27th ACM International Conference on Multimedia (MM \u201919), Nice, France.","DOI":"10.1145\/3343031.3350535"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature Pyramid Networks for Object Detection. arXiv.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_65","unstructured":"Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A.S., Bethge, M., and Brendel, W. (2020). Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming. arXiv."},{"key":"ref_66","unstructured":"Hendrycks, D., and Dietterich, T. (2019, January 6\u20139). Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1007\/s11119-019-09642-0","article-title":"Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of \u2018MangoYOLO\u2019","volume":"20","author":"Koirala","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.compag.2016.09.014","article-title":"Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors","volume":"130","author":"Underwood","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1002\/rob.21902","article-title":"A comparative study of fruit detection and counting methods for yield mapping in apple orchards","volume":"37","author":"Roy","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1590\/S0100-29452011000500014","article-title":"Situa\u00e7\u00e3o e perspectivas da fruticultura de clima temperado no Brasil","volume":"33","author":"Fachinello","year":"2011","journal-title":"Rev. Bras. Frutic."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Yahia, E.M. (2011). 6\u2014Feijoa (Acca sellowiana [Berg] Burret). Postharvest Biology and Technology of Tropical and Subtropical Fruits, Woodhead Publishing.","DOI":"10.1533\/9780857092618"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/54\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:46:09Z","timestamp":1760179569000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,25]]},"references-count":71,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13010054"],"URL":"https:\/\/doi.org\/10.3390\/rs13010054","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,25]]}}}