{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T22:44:41Z","timestamp":1771973081303,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005825","name":"United States National Institute of Food and Agriculture (NIFA)","doi-asserted-by":"publisher","award":["2020-67021-32461"],"award-info":[{"award-number":["2020-67021-32461"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster\u2019s distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.<\/jats:p>","DOI":"10.3390\/s24030970","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T09:42:32Z","timestamp":1706866952000},"page":"970","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping"],"prefix":"10.3390","volume":"24","author":[{"given":"Vaishnavi","family":"Thesma","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Georgia, Athens, GA 30602, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2497-5422","authenticated-orcid":false,"given":"Glen C.","family":"Rains","sequence":"additional","affiliation":[{"name":"Department of Entomology, University of Georgia Tifton Campus, Tifton, GA 31793, USA"}]},{"given":"Javad","family":"Mohammadpour Velni","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Clemson University, Clemson, SC 29634, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110209","DOI":"10.1109\/ACCESS.2021.3102227","article-title":"Big data and AI revolution in precision agriculture: Survey and challenges","volume":"9","author":"Bhat","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.compag.2018.08.001","article-title":"Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review","volume":"153","author":"Rieder","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103298","DOI":"10.1016\/j.agsy.2021.103298","article-title":"Big data in agriculture: Between opportunity and solution","volume":"195","author":"Osinga","year":"2022","journal-title":"Agric. Syst."},{"key":"ref_5","unstructured":"Chandra, A.L., Desai, S.V., Guo, W., and Balasubramanian, V.N. (2020). Computer vision with deep learning for plant phenotyping in agriculture: A survey. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"012053","DOI":"10.1088\/1757-899X\/1032\/1\/012053","article-title":"A review on the methods for big data analysis in agriculture","volume":"Volume 1032","author":"Evstatiev","year":"2021","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compag.2017.09.037","article-title":"A review on the practice of big data analysis in agriculture","volume":"143","author":"Kamilaris","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sishodia, R.P., Ray, R.L., and Singh, S.K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sens., 12.","DOI":"10.3390\/rs12193136"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1093\/aepp\/ppx056","article-title":"Big data in agriculture: A challenge for the future","volume":"40","author":"Coble","year":"2018","journal-title":"Appl. Econ. Perspect. Policy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1002\/agj2.20516","article-title":"Cotton boll distribution: A review","volume":"113","author":"Pabuayon","year":"2021","journal-title":"Agron. J."},{"key":"ref_11","unstructured":"(2019). Georgia Cotton Production Guide, University of Georgia."},{"key":"ref_12","unstructured":"Ritchie, G.L., Bednarz, C.W., Jost, P.H., and Brown, S.M. (2007). Cotton Growth and Development, University of Georgia."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.ifacol.2022.11.111","article-title":"Spatio-temporal Mapping of Cotton Blooms Appearance Using Deep Learning","volume":"55","author":"Thesma","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_14","unstructured":"Kadeghe, F., Glen, R., and Wesley, P. (2018, January 3\u20135). Real-Time 3-D Measurement of Cotton Boll Positions Using Machine Vision under Field Conditions. Proceedings of the Beltwide Cotton Conference, San Antonio, TX, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"341","DOI":"10.13031\/trans.13112","article-title":"Ensemble Method of Deep Learning, Color Segmentation, and Image Transformation to Track, Localize, and Count Cotton Bolls Using a Moving Camera in Real-Time","volume":"64","author":"Fue","year":"2021","journal-title":"Trans. ASABE"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.3389\/fpls.2017.02235","article-title":"Aerial images and convolutional neural network for cotton bloom detection","volume":"8","author":"Xu","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_18","unstructured":"Apache Software Foundation (2023). HBase, Apache Software Foundation."},{"key":"ref_19","unstructured":"Apache Software Foundation (2023). Cassandra, Apache Software Foundation."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"100265","DOI":"10.1016\/j.atech.2023.100265","article-title":"Development and Deployment of a Big Data Pipeline for Field-based High-throughput Cotton Phenotyping Data","volume":"5","author":"Issac","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_21","unstructured":"Pereira, M.F.L., Cruvinel, P.E., Alves, G.M., and Beraldo, J.M.G. (2020, January 3\u20135). Parallel computational structure and semantics for soil quality analysis based on LoRa and apache spark. Proceedings of the 2020 IEEE 14th International Conference on Semantic Computing (ICSC), San Diego, CA, USA."},{"key":"ref_22","first-page":"581","article-title":"Digital agriculture based on big data analytics: A focus on predictive irrigation for smart farming in Morocco","volume":"24","author":"Rabhi","year":"2021","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_23","unstructured":"Apache Software Foundation (2022). Hadoop, Apache Software Foundation."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"19955","DOI":"10.1007\/s11356-021-13248-3","article-title":"Agricultural Irrigation Recommendation and Alert (AIRA) system using optimization and machine learning in Hadoop for sustainable agriculture","volume":"29","author":"Veerachamy","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Zhang, Q., and Ye, Z. (2019, January 29\u201331). Research on the application of agricultural big data processing with Hadoop and Spark. Proceedings of the 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China.","DOI":"10.1109\/ICAICA.2019.8873519"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17485\/ijst\/2016\/v9i47\/101745","article-title":"Big data analytics recommendation solutions for crop disease using Hive and Hadoop Platform","volume":"9","author":"Garg","year":"2016","journal-title":"Indian J. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sahu, S., Chawla, M., and Khare, N. (2017, January 5\u20136). An efficient analysis of crop yield prediction using Hadoop framework based on random forest approach. Proceedings of the 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India.","DOI":"10.1109\/CCAA.2017.8229770"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ye, Z., and Zheng, K. (2021). A parallel computing approach to spatial neighboring analysis of large amounts of terrain data using spark. Sensors, 21.","DOI":"10.3390\/s21020365"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_30","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_31","unstructured":"Apache Software Foundation (2023). Spark, Apache Software Foundation."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/970\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:53:29Z","timestamp":1760104409000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/970"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,2]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030970"],"URL":"https:\/\/doi.org\/10.3390\/s24030970","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,2]]}}}