{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:22:05Z","timestamp":1727065325011},"reference-count":170,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit\/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.<\/jats:p>","DOI":"10.3390\/rs13030531","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T18:01:12Z","timestamp":1612288872000},"page":"531","source":"Crossref","is-referenced-by-count":59,"title":["Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5519-8240","authenticated-orcid":false,"given":"Caiwang","family":"Zheng","sequence":"first","affiliation":[{"name":"Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA"},{"name":"School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32603, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6182-4017","authenticated-orcid":false,"given":"Amr","family":"Abd-Elrahman","sequence":"additional","affiliation":[{"name":"Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA"},{"name":"School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32603, USA"}]},{"given":"Vance","family":"Whitaker","sequence":"additional","affiliation":[{"name":"Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA"},{"name":"Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"ref_1","unstructured":"FAO (2018). The Future of Food and Agriculture\u2014Alternative Pathways to 2050, Food and Agriculture Organization of the United Nations."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1023\/B:PRAG.0000040806.39604.aa","article-title":"Precision agriculture and sustainability","volume":"5","author":"Bongiovanni","year":"2004","journal-title":"Precis. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","article-title":"Precision agriculture\u2014A worldwide overview","volume":"36","author":"Zhang","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3844\/ajabssp.2010.50.55","article-title":"A review: The role of remote sensing in precision agriculture","volume":"5","author":"Liaghat","year":"2010","journal-title":"Am. J. Agric. Biol. Sci."},{"key":"ref_5","first-page":"7","article-title":"Adoption of precision agriculture technologies in developed and developing countries","volume":"8","author":"Say","year":"2018","journal-title":"Online J. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.3389\/fpls.2018.01933","article-title":"Plant phenotyping research trends, a science mapping approach","volume":"9","author":"Costa","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.3389\/fpls.2017.01111","article-title":"Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives","volume":"8","author":"Yang","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chawade, A., van Ham, J., Blomquist, H., Bagge, O., Alexandersson, E., and Ortiz, R. (2019). High-throughput field-phenotyping tools for plant breeding and precision agriculture. Agronomy, 9.","DOI":"10.3390\/agronomy9050258"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12038-020-00083-w","article-title":"Plant phenomics: High-throughput technology for accelerating genomics","volume":"45","author":"Pasala","year":"2020","journal-title":"J. Biosci."},{"key":"ref_10","first-page":"622","article-title":"The quest for understanding phenotypic variation via integrated approaches in the field environment","volume":"172","author":"Pauli","year":"2016","journal-title":"Plant Physiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.molp.2020.01.008","article-title":"Crop phenomics and high-throughput phenotyping: Past decades, current challenges, and future perspectives","volume":"13","author":"Yang","year":"2020","journal-title":"Mol. Plant"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1080\/10942912.2020.1716793","article-title":"Nondestructive detection of storage time of strawberries using visible\/near-infrared hyperspectral imaging","volume":"23","author":"Weng","year":"2020","journal-title":"Int. J. Food Prop."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"205","DOI":"10.3233\/JBR-180314","article-title":"Status of strawberry breeding programs and cultivation systems in Europe and the rest of the world","volume":"8","author":"Mezzetti","year":"2018","journal-title":"J. Berry Res."},{"key":"ref_14","unstructured":"Food and Agriculture Organization of the United Nations (2020, November 20). FAOSTAT Database; 2018. Available online: http:\/\/www.fao.org\/faostat\/en\/?#data\/QC."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1016\/S2095-3119(17)61859-8","article-title":"Agricultural remote sensing big data: Management and applications","volume":"17","author":"Huang","year":"2018","journal-title":"J. Integr. Agric."},{"key":"ref_16","first-page":"101972","article-title":"Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces","volume":"84","author":"Sicre","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2016.11.001","article-title":"Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields","volume":"123","author":"Xu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Luo, Y., Xu, Y.-P., Tian, Y., and Yang, T. (2019). Satellite soil moisture for agricultural drought monitoring: Assessment of SMAP-derived soil water deficit index in Xiang River Basin, China. Remote. Sens., 11.","DOI":"10.3390\/rs11030362"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Du, T.L.T., Bui, D.D., Nguyen, M.D., and Lee, H. (2018). Satellite-based, multi-indices for evaluation of agricultural droughts in a highly dynamic tropical catchment, Central Vietnam. Water, 10.","DOI":"10.3390\/w10050659"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"074020","DOI":"10.1088\/1748-9326\/aacc7a","article-title":"Combining satellite data and agricultural statistics to map grassland management intensity in Europe","volume":"13","author":"Estel","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_21","first-page":"147","article-title":"Forecast of wheat yield throughout the agricultural season using optical and radar satellite images","volume":"59","author":"Fieuzal","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sharma, A.K., Hubert-Moy, L., Buvaneshwari, S., Sekhar, M., Ruiz, L., Bandyopadhyay, S., and Corgne, S. (2018). Irrigation history estimation using multitemporal landsat satellite images: Application to an intensive groundwater irrigated agricultural watershed in India. Remote. Sens., 10.","DOI":"10.3390\/rs10060893"},{"key":"ref_23","first-page":"187","article-title":"Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery","volume":"80","author":"Xie","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111460","DOI":"10.1016\/j.rse.2019.111460","article-title":"Synergistic integration of optical and microwave satellite data for crop yield estimation","volume":"234","author":"Piles","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107148","DOI":"10.1016\/j.comnet.2020.107148","article-title":"A compilation of UAV applications for precision agriculture","volume":"172","author":"Sarigiannidis","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_26","first-page":"307","article-title":"Deployment and Performance of a UAV for Crop Spraying","volume":"44","author":"Giles","year":"2015","journal-title":"Chem. Eng. Trans."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.compag.2017.04.011","article-title":"An adaptive approach for UAV-based pesticide spraying in dynamic environments","volume":"138","author":"Freitas","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.scienta.2017.04.024","article-title":"Multisensor approach to assess vineyard thermal dynamics combining high-resolution unmanned aerial vehicle (UAV) remote sensing and wireless sensor network (WSN) proximal sensing","volume":"221","author":"Matese","year":"2017","journal-title":"Sci. Hortic."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Popescu, D., Stoican, F., Stamatescu, G., Ichim, L., and Dragana, C. (2020). Advanced UAV\u2013WSN System for Intelligent Monitoring in Precision Agriculture. Sensors, 20.","DOI":"10.3390\/s20030817"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3390\/rs3030570","article-title":"Design and development of a multi-purpose low-cost hyperspectral imaging system","volume":"3","author":"Vallad","year":"2011","journal-title":"Remote. Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jin, X., Li, Z., and Atzberger, C. (2020). Editorial for the Special Issue \u201cEstimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12060940"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote. Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.biosystemseng.2017.09.009","article-title":"Close range hyperspectral imaging of plants: A review","volume":"164","author":"Mishra","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/S0034-4257(03)00125-1","article-title":"Fluorescence sensing systems: In vivo detection of biophysical variations in field corn due to nitrogen supply","volume":"86","author":"Corp","year":"2003","journal-title":"Remote. Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.3390\/rs4061519","article-title":"Development of a UAV-LiDAR system with application to forest inventory","volume":"4","author":"Wallace","year":"2012","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/JSTARS.2016.2639043","article-title":"Radar remote sensing of agricultural canopies: A review","volume":"10","author":"McNairn","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ban, Y. (2016). A review of multitemporal synthetic aperture radar (SAR) for crop monitoring. Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, Springer.","DOI":"10.1007\/978-3-319-47037-5"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2095-3119(18)62016-7","article-title":"Research advances of SAR remote sensing for agriculture applications: A review","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_39","unstructured":"Kuester, M., Thome, K., Krause, K., Canham, K., and Whittington, E. (2001, January 9\u201313). Comparison of surface reflectance measurements from three ASD FieldSpec FR spectroradiometers and one ASD FieldSpec VNIR spectroradiometer. Proceedings of the IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), Sydney, NSW, Australia."},{"key":"ref_40","unstructured":"Danner, M., Locherer, M., Hank, T., and Richter, K. (2015). Spectral Sampling with the ASD FieldSpec 4\u2014Theory, Measurement, Problems, Interpretation, GFZ Data Services. EnMAP Field Guides Technical Report."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mahmud, M.S., Zaman, Q.U., Esau, T.J., Chang, Y.K., Price, G.W., and Prithiviraj, B. (2020). Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System. Agronomy, 10.","DOI":"10.3390\/agronomy10071027"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"giy153","DOI":"10.1093\/gigascience\/giy153","article-title":"Computer vision-based phenotyping for improvement of plant productivity: A machine learning perspective","volume":"8","author":"Mochida","year":"2019","journal-title":"GigaScience"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","article-title":"Feature selection in machine learning: A new perspective","volume":"300","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1016\/j.procs.2016.07.111","article-title":"A survey on feature selection","volume":"91","author":"Miao","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2588","DOI":"10.1002\/jsfa.8080","article-title":"Computer vision-based method for classification of wheat grains using artificial neural network","volume":"97","author":"Sabanci","year":"2017","journal-title":"J. Sci. Food Agric."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning\u2013Method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1111\/ced.14029","article-title":"Artificial intelligence, machine learning and deep learning: Definitions and differences","volume":"45","author":"Jakhar","year":"2020","journal-title":"Clin. Exp. Dermatol."},{"key":"ref_50","unstructured":"Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., and Duffy, N. (2016). Evolving deep neural networks. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Seifert, C., Aamir, A., Balagopalan, A., Jain, D., Sharma, A., Grottel, S., and Gumhold, S. (2017). Visualizations of deep neural networks in computer vision: A survey. Transparent Data Mining for Big and Small Data, Springer.","DOI":"10.1007\/978-3-319-54024-5_6"},{"key":"ref_52","unstructured":"Zhang, J., and Man, K.F. (1998, January 14). Time series prediction using RNN in multi-dimension embedding phase space. Proceedings of the SMC\u201998 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218), San Diego, CA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","article-title":"Convolutional neural networks for hyperspectral image classification","volume":"219","author":"Yu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liu, T., and Abd-Elrahman, A. (2018). An object-based image analysis method for enhancing classification of land covers using fully convolutional networks and multi-view images of small unmanned aerial system. Remote. Sens., 10.","DOI":"10.3390\/rs10030457"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1146\/annurev-statistics-010814-020120","article-title":"Learning deep generative models","volume":"2","author":"Salakhutdinov","year":"2015","journal-title":"Ann. Rev. Stat. Appl."},{"key":"ref_56","unstructured":"Pu, Y., Gan, Z., Henao, R., Yuan, X., Li, C., Stevens, A., and Carin, L. (2016). Variational autoencoder for deep learning of images, labels and captions. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41438-019-0151-5","article-title":"Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production","volume":"6","author":"Bauer","year":"2019","journal-title":"Hortic. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote. Sens. Mag."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","unstructured":"Puttemans, S., Vanbrabant, Y., Tits, L., and Goedem\u00e9, T. (2016, January 12\u201315). Automated visual fruit detection for harvest estimation and robotic harvesting. Proceedings of the 2016 sixth international conference on image processing theory, tools and applications (IPTA), Oulu, Finland.","DOI":"10.1109\/IPTA.2016.7820996"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4","DOI":"10.5772\/5662","article-title":"Fruit detachment and classification method for strawberry harvesting robot","volume":"5","author":"Feng","year":"2008","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lin, P., and Chen, Y. (2018, January 27\u201329). Detection of Strawberry Flowers in Outdoor Field by Deep Neural Network. Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, China.","DOI":"10.1109\/ICIVC.2018.8492793"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lamb, N., and Chuah, M.C. (2018, January 10\u201313). A strawberry detection system using convolutional neural networks. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622466"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"104846","DOI":"10.1016\/j.compag.2019.06.001","article-title":"Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN","volume":"163","author":"Yu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"559","DOI":"10.3389\/fpls.2020.00559","article-title":"A novel greenhouse-based system for the detection and plumpness assessment of strawberry using an improved deep learning technique","volume":"11","author":"Zhou","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1016\/j.foodchem.2005.12.005","article-title":"Quality characteristics of strawberry genotypes at different maturation stages","volume":"100","author":"Kafkas","year":"2007","journal-title":"Food Chem."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s00217-003-0822-0","article-title":"Changes in flavour and texture during the ripening of strawberries","volume":"218","author":"Azodanlou","year":"2004","journal-title":"Eur. Food Res. Technol."},{"key":"ref_68","unstructured":"Kader, A.A. (1991). Quality and its maintenance in relation to the postharvest physiology of strawberry. The Strawberry into the 21st Century, Timber Press."},{"key":"ref_69","first-page":"28","article-title":"Maturity stages affect the postharvest quality and shelf-life of fruits of strawberry genotypes growing in subtropical regions","volume":"15","author":"Rahman","year":"2016","journal-title":"J. Saudi Soc. Agric. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3390\/plants7010003","article-title":"Advances in non-destructive early assessment of fruit ripeness towards defining optimal time of harvest and yield prediction\u2014A review","volume":"7","author":"Li","year":"2018","journal-title":"Plants"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.tifs.2007.03.011","article-title":"Extending and measuring the quality of fresh-cut fruit and vegetables: A review","volume":"18","author":"Rico","year":"2007","journal-title":"Trends Food Sci. Technol."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Kader, A.A. (2002). Quality parameters of fresh-cut fruit and vegetable products. Fresh-Cut Fruits and Vegetables, CRC Press.","DOI":"10.1201\/9781420031874.ch2"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Liu, C., Liu, W., Lu, X., Ma, F., Chen, W., Yang, J., and Zheng, L. (2014). Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0087818"},{"key":"ref_74","unstructured":"Bai, J., Plotto, A., Baldwin, E., Whitaker, V., and Rouseff, R. (2010, January 6\u20138). Electronic nose for detecting strawberry fruit maturity. Proceedings of the Florida State Horticultural Society, Crystal River, FL, USA."},{"key":"ref_75","first-page":"1540","article-title":"Assessment of Fruit Maturity using Direct Color Mapping","volume":"3","author":"Raut","year":"2016","journal-title":"Int. Res. J. Eng. Technol."},{"key":"ref_76","first-page":"1423","article-title":"Identification of strawberry ripeness based on multispectral indexes extracted from hyperspectral images","volume":"36","author":"Jiang","year":"2016","journal-title":"Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jfoodeng.2016.01.002","article-title":"Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine","volume":"179","author":"Guo","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_78","unstructured":"Yue, X.-Q., Shang, Z.-Y., Yang, J.-Y., Huang, L., and Wang, Y.-Q. (2019). A smart data-driven rapid method to recognize the strawberry maturity. Inf. Proc. Agric."},{"key":"ref_79","first-page":"31","article-title":"Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning","volume":"4","author":"Gao","year":"2020","journal-title":"Artif. Intell. Agric."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.compag.2019.01.009","article-title":"Development and field evaluation of a strawberry harvesting robot with a cable-driven gripper","volume":"157","author":"Xiong","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"75","DOI":"10.14419\/ijet.v7i4.40.24080","article-title":"Evaluation of deep convolutional neural network architectures for strawberry quality inspection","volume":"7","author":"Sustika","year":"2018","journal-title":"Int. J. Eng. Technol."},{"key":"ref_82","first-page":"53","article-title":"Automated Sorting and Grading of Vegetables Using Image Processing","volume":"5","author":"Usha","year":"2017","journal-title":"Int. J. Eng. Res. Gen. Sci."},{"key":"ref_83","first-page":"48","article-title":"Experimental on storage and preservation of strawberry","volume":"36","author":"Shen","year":"2011","journal-title":"Food Sci. Tech"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"S32","DOI":"10.1016\/j.compag.2009.09.013","article-title":"Automated strawberry grading system based on image processing","volume":"71","author":"Liming","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Mahendra, O., Pardede, H.F., Sustika, R., and Kusumo, R.B.S. (2018, January 1\u20132). Comparison of Features for Strawberry Grading Classification with Novel Dataset. Proceedings of the 2018 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), Tangerang, Indonesia.","DOI":"10.1109\/IC3INA.2018.8629534"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.postharvbio.2007.02.001","article-title":"A comprehensive approach to evaluate the freshness of strawberries and carrots","volume":"45","author":"Brockhoff","year":"2007","journal-title":"Postharvest Biol. Technol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1038\/srep02585","article-title":"Analyzing strawberry spoilage via its volatile compounds using longpath fourier transform infrared spectroscopy","volume":"3","author":"Dong","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least-squares regression: A tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"11889","DOI":"10.3390\/s150511889","article-title":"Fruit quality evaluation using spectroscopy technology: A review","volume":"15","author":"Wang","year":"2015","journal-title":"Sensors"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.jfoodeng.2006.10.016","article-title":"Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry","volume":"81","author":"ElMasry","year":"2007","journal-title":"J. Food Eng."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Weng, S., Yu, S., Guo, B., Tang, P., and Liang, D. (2020). Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods. Sensors, 20.","DOI":"10.3390\/s20113074"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1007\/s12161-018-01430-2","article-title":"Near-infrared hyperspectral imaging rapidly detects the decay of postharvest strawberry based on water-soluble sugar analysis","volume":"12","author":"Liu","year":"2019","journal-title":"Food Anal. Methods"},{"key":"ref_93","first-page":"1390","article-title":"Prediction and analysis of strawberry sugar content based on partial least squares prediction model","volume":"29","author":"Liu","year":"2019","journal-title":"J. Anim. Plant Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.postharvbio.2016.11.013","article-title":"Potential of NIR spectroscopy for predicting internal quality and discriminating among strawberry fruits from different production systems","volume":"125","author":"Amodio","year":"2017","journal-title":"Postharvest Biol. Technol."},{"key":"ref_95","first-page":"372","article-title":"Near-infrared spectra combining with CARS and SPA algorithms to screen the variables and samples for quantitatively determining the soluble solids content in strawberry","volume":"35","author":"LI","year":"2015","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_96","first-page":"1020","article-title":"Determination of soluble solid content in strawberry using hyperspectral imaging combined with feature extraction methods","volume":"35","author":"Ding","year":"2015","journal-title":"Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.jfoodeng.2011.12.003","article-title":"Non-destructive characterization and quality control of intact strawberries based on NIR spectral data","volume":"110","year":"2012","journal-title":"J. Food Eng."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3136\/nskkk.56.229","article-title":"Non-destructive analysis of soluble sugar components in strawberry fruits using near-infrared spectroscopy","volume":"56","author":"Nishizawa","year":"2009","journal-title":"Nippon Shokuhin Kagaku Kogaku Kaishi = J. Jpn. Soc. Food Sci. Technol."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1021\/jf072495i","article-title":"Nondestructive application of laser-induced fluorescence spectroscopy for quantitative analyses of phenolic compounds in strawberry fruits (Fragaria \u00d7 ananassa)","volume":"56","author":"Wulf","year":"2008","journal-title":"J. Agric. Food Chem."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2525\/ecb.44.245","article-title":"Non-destructive estimation of firmness of strawberries (Fragaria \u00d7 ananassa Duch.) using NIR hyperspectral imaging","volume":"44","author":"Tallada","year":"2006","journal-title":"Environ. Control. Biol."},{"key":"ref_101","unstructured":"Nagata, M., Tallada, J.G., Kobayashi, T., and Toyoda, H. (2005, January 17\u201320). NIR hyperspectral imaging for measurement of internal quality in strawberries. Proceedings of the 2005 ASAE Annual Meeting, Tampa, FL, USA. ASAE Paper No. 053131."},{"key":"ref_102","unstructured":"Nagata, M., Tallada, J.G., Kobayashi, T., Cui, Y., and Gejima, Y. (2004, January 1\u20134). Predicting maturity quality parameters of strawberries using hyperspectral imaging. Proceedings of the ASAE\/CSAE Annual International Meeting, Ottawa, ON, Canada. Paper No. 043033."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Ishikawa, T., Hayashi, A., Nagamatsu, S., Kyutoku, Y., Dan, I., Wada, T., Oku, K., Saeki, Y., Uto, T., and Tanabata, T. (2018). Classification of strawberry fruit shape by machine learning. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci., 42.","DOI":"10.5194\/isprs-archives-XLII-2-463-2018"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.biosystemseng.2018.04.004","article-title":"A simple and efficient method for automatic strawberry shape and size estimation and classification","volume":"170","author":"Oo","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"giaa030","DOI":"10.1093\/gigascience\/giaa030","article-title":"Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry","volume":"9","author":"Feldmann","year":"2020","journal-title":"GigaScience"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-017-0243-x","article-title":"A novel 3D imaging system for strawberry phenotyping","volume":"13","author":"He","year":"2017","journal-title":"Plant Methods"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"395","DOI":"10.20965\/ijat.2018.p0395","article-title":"A 3D shape-measuring system for assessing strawberry fruits","volume":"12","author":"Kochi","year":"2018","journal-title":"Int. J. Autom. Technol."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Li, B., Cockerton, H.M., Johnson, A.W., Karlstr\u00f6m, A., Stavridou, E., Deakin, G., and Harrison, R.J. (2020). Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping. bioRxiv.","DOI":"10.1101\/2020.03.01.972190"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/9525204","article-title":"Evaluating correlations and development of meteorology based yield forecasting model for strawberry","volume":"2016","author":"Pathak","year":"2016","journal-title":"Adv. Meteorol."},{"key":"ref_110","unstructured":"Misaghi, F., Dayyanidardashti, S., Mohammadi, K., and Ehsani, M. (2004). Application of Artificial Neural Network and Geostatistical Methods in Analyzing Strawberry Yield Data, American Society of Agricultural and Biological Engineers."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"278","DOI":"10.2134\/agronj2008.0208","article-title":"A method to predict weekly strawberry fruit yields from extended season production systems","volume":"101","author":"MacKenzie","year":"2009","journal-title":"Agron. J."},{"key":"ref_112","first-page":"228","article-title":"Comparative the impact of organic and conventional strawberry cultivation on growth and productivity using remote sensing techniques under Egypt climate conditions","volume":"6","author":"Hassan","year":"2018","journal-title":"Asian J. Agric. Biol."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Maskey, M.L., Pathak, T.B., and Dara, S.K. (2019). Weather Based Strawberry Yield Forecasts at Field Scale Using Statistical and Machine Learning Models. Atmosphere, 10.","DOI":"10.3390\/atmos10070378"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Chen, Y., Lee, W.S., Gan, H., Peres, N., Fraisse, C., Zhang, Y., and He, Y. (2019). Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages. Remote. Sens., 11.","DOI":"10.3390\/rs11131584"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/esp.3366","article-title":"Topographic structure from motion: A new development in photogrammetric measurement","volume":"38","author":"Fonstad","year":"2013","journal-title":"Earth Surf. Proc. Landf."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Ozyesil, O., Voroninski, V., Basri, R., and Singer, A. (2017). A survey of structure from motion. arXiv.","DOI":"10.1017\/S096249291700006X"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Patrick, A., and Li, C. (2017). High throughput phenotyping of blueberry bush morphological traits using unmanned aerial systems. Remote. Sens., 9.","DOI":"10.3390\/rs9121250"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Makanza, R., Zaman-Allah, M., Cairns, J.E., Magorokosho, C., Tarekegne, A., Olsen, M., and Prasanna, B.M. (2018). High-throughput phenotyping of canopy cover and senescence in maize field trials using aerial digital canopy imaging. Remote. Sens., 10.","DOI":"10.3390\/rs10020330"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"926","DOI":"10.3389\/fpls.2019.00926","article-title":"Fuzzy Clustering of Maize Plant-Height Patterns Using Time Series of UAV Remote-Sensing Images and Variety Traits","volume":"10","author":"Han","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/1353691","article-title":"Significant remote sensing vegetation indices: A review of developments and applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_121","first-page":"103","article-title":"A visible band index for remote sensing leaf chlorophyll content at the canopy scale","volume":"21","author":"Hunt","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/JSTARS.2011.2176468","article-title":"Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content","volume":"5","author":"Clevers","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-43011-1","article-title":"Radiative transfer modelling reveals why canopy reflectance follows function","volume":"9","author":"Kattenborn","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Yuan, H., Yang, G., Li, C., Wang, Y., Liu, J., Yu, H., Feng, H., Xu, B., Zhao, X., and Yang, X. (2017). Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models. Remote. Sens., 9.","DOI":"10.3390\/rs9040309"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2019.03.002","article-title":"Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations","volume":"225","author":"Wolanin","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2447","DOI":"10.1080\/00103620600820097","article-title":"Estimating the nitrogen concentration of strawberry plants from its spectral response","volume":"37","author":"Lobit","year":"2006","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1590\/1807-1929\/agriambi.v20n8p716-721","article-title":"Method for estimating leaf coverage in strawberry plants using digital image processing","volume":"20","author":"Sandino","year":"2016","journal-title":"Rev. Bras. Eng. Agr\u00edcola Ambient."},{"key":"ref_128","first-page":"99","article-title":"A new multi-scale analytic algorithm for edge extraction of strawberry leaf images in natural light","volume":"9","author":"Jianlun","year":"2016","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2020.02.021","article-title":"Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images","volume":"163","author":"Guan","year":"2020","journal-title":"J. Photogramm. Remote. Sens."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Abd-Elrahman, A., Guan, Z., Dalid, C., Whitaker, V., Britt, K., Wilkinson, B., and Gonzalez, A. (2020). Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery. Remote. Sens., 12.","DOI":"10.3390\/rs12213632"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"31","DOI":"10.2525\/ecb.58.31","article-title":"Quantification of Strawberry Plant Growth and Amount of Light Received Using a Depth Sensor","volume":"58","author":"Takahashi","year":"2020","journal-title":"Environ. Control. Biol."},{"key":"ref_132","unstructured":"Kokin, E., Palge, V., Pennar, M., and J\u00fcrjenson, K. (2018). Strawberry leaf surface temperature dynamics measured by thermal camera in night frost conditions. Agron. Res., 16."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.compag.2013.08.018","article-title":"A fuzzy logic based irrigation system enhanced with wireless data logging applied to the state of Qatar","volume":"98","author":"Touati","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Av\u015far, E., Bulu\u015f, K., Sarida\u015f, M.A., and Kapur, B. (2018, January 7\u20139). Development of a cloud-based automatic irrigation system: A case study on strawberry cultivation. Proceedings of the 2018 7th International Conference on Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, Greece.","DOI":"10.1109\/MOCAST.2018.8376641"},{"key":"ref_135","first-page":"166","article-title":"Automated irrigation system using a wireless sensor network and GPRS module","volume":"63","year":"2013","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.agwat.2014.09.021","article-title":"Toward precision irrigation for intensive strawberry cultivation","volume":"151","author":"Morillo","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Gerhards, M., Schlerf, M., Mallick, K., and Udelhoven, T. (2019). Challenges and future perspectives of multi-\/Hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote. Sens., 11.","DOI":"10.3390\/rs11101240"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.envexpbot.2012.04.004","article-title":"Physiological and growth responses to water deficits in cultivated strawberry (Fragaria\u00d7 ananassa) and in one of its progenitors, Fragaria chiloensis","volume":"83","author":"Grant","year":"2012","journal-title":"Environ. Exp. Bot."},{"key":"ref_139","first-page":"79","article-title":"The impact of drought stress on morphological and physiological parameters of three strawberry varieties in different growing conditions","volume":"52","author":"Nezhadahmadi","year":"2015","journal-title":"Pak. J. Agric. Sci."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.envexpbot.2010.01.008","article-title":"Physiological and morphological diversity of cultivated strawberry (Fragaria\u00d7 ananassa) in response to water deficit","volume":"68","author":"Grant","year":"2010","journal-title":"Environ. Exp. Bot."},{"key":"ref_141","first-page":"179","article-title":"Response to drought stress of three strawberry cultivars grown under greenhouse conditions","volume":"16","author":"Klamkowski","year":"2008","journal-title":"J. Fruit Ornam. Plant Res."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1002\/jsfa.8471","article-title":"Yield, quality and biochemical properties of various strawberry cultivars under water stress","volume":"98","author":"Adak","year":"2018","journal-title":"J. Sci. Food Agric."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/0168-1923(92)90111-G","article-title":"Remotely measured canopy temperature of greenhouse strawberries as indicator of water status and yield under mild and very mild water stress conditions","volume":"58","author":"Serrano","year":"1992","journal-title":"Agric. For. Meteorol."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s11099-008-0108-7","article-title":"Chlorophyll fluorescence as a tool for evaluation of drought stress in strawberry","volume":"46","author":"Razavi","year":"2008","journal-title":"Photosynthetica"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1017\/S2040470017001297","article-title":"High resolution strawberry field monitoring using the compact hyperspectral imaging solution COSI","volume":"8","author":"Delalieux","year":"2017","journal-title":"Adv. Anim. Biosci."},{"key":"ref_146","first-page":"159","article-title":"Automatic diagnosis of strawberry water stress status based on machine vision","volume":"12","author":"Li","year":"2019","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Gerhards, M., Schlerf, M., Rascher, U., Udelhoven, T., Juszczak, R., Alberti, G., Miglietta, F., and Inoue, Y. (2018). Analysis of airborne optical and thermal imagery for detection of water stress symptoms. Remote. Sens., 10.","DOI":"10.3390\/rs10071139"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Oliveira, M.S., and Peres, N.A. (2020). Common Strawberry Diseases in Florida. EDIS, 2020.","DOI":"10.32473\/edis-pp354-2020"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"434","DOI":"10.3390\/agriengineering1030032","article-title":"Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection","volume":"1","author":"Chang","year":"2019","journal-title":"AgriEngineering"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","article-title":"Plant Disease detection by imaging sensors\u2013parallels and specific demands for precision agriculture and plant phenotyping","volume":"100","author":"Mahlein","year":"2016","journal-title":"Plant Dis."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Park, H., Eun, J.-S., and Kim, S.-H. (2017, January 18\u201320). Image-based disease diagnosing and predicting of the crops through the deep learning mechanism. Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.","DOI":"10.1109\/ICTC.2017.8190957"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.biosystemseng.2020.03.016","article-title":"Effect of directional augmentation using supervised machine learning technologies: A case study of strawberry powdery mildew detection","volume":"194","author":"Shin","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"De Lange, E.S., and Nansen, C. (2019, January 8\u201310). Early detection of arthropod-induced stress in strawberry using innovative remote sensing technology. Proceedings of the GeoVet 2019. Novel Spatio-Temporal Approaches in the Era of Big Data, Davis, CA, USA.","DOI":"10.3389\/conf.fvets.2019.05.00104"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.postharvbio.2019.03.017","article-title":"Information fusion of hyperspectral imaging and electronic nose for evaluation of fungal contamination in strawberries during decay","volume":"153","author":"Liu","year":"2019","journal-title":"Postharvest Biol. Technol."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Cockerton, H.M., Li, B., Vickerstaff, R., Eyre, C.A., Sargent, D.J., Armitage, A.D., Marina-Montes, C., Garcia, A., Passey, A.J., and Simpson, D.W. (2018). Image-based Phenotyping and Disease Screening of Multiple Populations for resistance to Verticillium dahliae in cultivated strawberry Fragaria x ananassa. bioRxiv, 497107.","DOI":"10.1101\/497107"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Alt\u0131parmak, H., Al Shahadat, M., Kiani, E., and Dimililer, K. (2017, January 13\u201315). Fuzzy classification for strawberry diseases-infection using machine vision and soft-computing techniques. Proceedings of the Tenth International Conference on Machine Vision (ICMV 2017), Vienna, Austria.","DOI":"10.1117\/12.2309837"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Hecht-Nielsen, R. (, 1989). Theory of the backpropagation neural network. Proceedings of the International 1989 Joint Conference on Neural Networks, Washington, DC, USA.","DOI":"10.1109\/IJCNN.1989.118638"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.postharvbio.2018.01.018","article-title":"Detection of fungal infections in strawberry fruit by VNIR\/SWIR hyperspectral imaging","volume":"139","author":"Siedliska","year":"2018","journal-title":"Postharvest Biol. Technol."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Thompson, B. (1995). Stepwise Regression and Stepwise Discriminant Analysis Need Not Apply Here: A Guidelines, Sage Publications.","DOI":"10.1177\/0013164495055004001"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.compag.2017.01.017","article-title":"Field detection of anthracnose crown rot in strawberry using spectroscopy technology","volume":"135","author":"Lu","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"167","DOI":"10.3923\/ajppaj.2017.167.173","article-title":"Spectral and molecular studies on gray mold in strawberry","volume":"11","author":"Aboelghar","year":"2017","journal-title":"Asian J. Plant Pathol."},{"key":"ref_162","unstructured":"Yuhas, R.H., Goetz, A.F.H., and Boardman, J.W. (1992). Discrimination among semi-arid landscape endmembers using the Spectral AngleMapper (SAM) algorithm. Summaries of the Third Annual JPL Airborne Geoscience Workshop, AVIRIS Workshop."},{"key":"ref_163","first-page":"35","article-title":"Self-developed QWL measures","volume":"4","author":"Levine","year":"1983","journal-title":"J. Occup. Behav."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2016.01.012","article-title":"Strawberry foliar anthracnose assessment by hyperspectral imaging","volume":"122","author":"Yeh","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"361","DOI":"10.3182\/20130327-3-JP-3017.00081","article-title":"A comparison of machine learning methods on hyperspectral plant disease assessments","volume":"46","author":"Yeh","year":"2013","journal-title":"IFAC Proc. Vol."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41438-019-0123-9","article-title":"3D point cloud data to quantitatively characterize size and shape of shrub crops","volume":"6","author":"Jiang","year":"2019","journal-title":"Hortic. Res."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1109\/JSTARS.2020.2983000","article-title":"Canopy Averaged Chlorophyll Content Prediction of Pear Trees Using Convolutional Autoencoder on Hyperspectral Data","volume":"13","author":"Paul","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"105459","DOI":"10.1016\/j.compag.2020.105459","article-title":"Modern imaging techniques in plant nutrition analysis: A review","volume":"174","author":"Li","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"112083","DOI":"10.1016\/j.rse.2020.112083","article-title":"Comparison of total emitted solar-induced chlorophyll fluorescence (SIF) and top-of-canopy (TOC) SIF in estimating photosynthesis","volume":"251","author":"Lu","year":"2020","journal-title":"Remote. Sens. Environ."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"111733","DOI":"10.1016\/j.rse.2020.111733","article-title":"Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops","volume":"241","author":"Dechant","year":"2020","journal-title":"Remote. Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/531\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:23:55Z","timestamp":1724415835000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/531"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,2]]},"references-count":170,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13030531"],"URL":"https:\/\/doi.org\/10.3390\/rs13030531","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,2,2]]}}}