{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T12:54:16Z","timestamp":1782737656563,"version":"3.54.5"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Texas State Support Committee and Cotton Incorporated","award":["19-846TX"],"award-info":[{"award-number":["19-846TX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production.<\/jats:p>","DOI":"10.3390\/rs16111906","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T05:14:02Z","timestamp":1716786842000},"page":"1906","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9115-4042","authenticated-orcid":false,"given":"Sambandh Bhusan","family":"Dhal","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stavros","family":"Kalafatis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulisses","family":"Braga-Neto","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna Chaitanya","family":"Gadepally","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6327-6093","authenticated-orcid":false,"given":"Jose Luis","family":"Landivar-Scott","sequence":"additional","affiliation":[{"name":"Texas A&M AgriLife Research and Extension Center, Corpus Christi, TX 77406, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Texas A&M AgriLife Research and Extension Center, Corpus Christi, TX 77406, USA"},{"name":"Department of Computer Science, Texas A&M University, Corpus Christi, TX 78412, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Nowka","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Landivar","sequence":"additional","affiliation":[{"name":"Texas A&M AgriLife Research and Extension Center, Corpus Christi, TX 77406, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pankaj","family":"Pal","sequence":"additional","affiliation":[{"name":"Texas A&M AgriLife Research and Extension Center, Corpus Christi, TX 77406, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahendra","family":"Bhandari","sequence":"additional","affiliation":[{"name":"Texas A&M AgriLife Research and Extension Center, Corpus Christi, TX 77406, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"ref_1","unstructured":"McDonald, B.L. (2010). Food Security, Polity."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mahanta, S., Habib, M.R., and Moore, J.M. (2022). Effect of high-voltage atmospheric cold plasma treatment on germination and heavy metal uptake by soybeans (Glycine max). Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23031611"},{"key":"ref_3","unstructured":"Dutta, A., Dahal, P., Prajapati, R., Tamang, P., and Kumar, E.S. (2018, January 27). IoT based aquaponics monitoring system. Proceedings of the 1st KEC Conference Proceedings, Lalitpur, Nepal."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dhal, S.B., Jungbluth, K., Lin, R., Sabahi, S.P., Bagavathiannan, M., Braga-Neto, U., and Kalafatis, S. (2022). A machine-learning-based IoT system for optimizing nutrient supply in commercial aquaponic operations. Sensors, 22.","DOI":"10.20944\/preprints202203.0039.v1"},{"key":"ref_5","first-page":"68","article-title":"Nutrient optimization for plant growth in Aquaponic irrigation using machine learning for small training datasets","volume":"6","author":"Dhal","year":"2022","journal-title":"Artif. Intell. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dhal, S.B., Bagavathiannan, M., Braga-Neto, U., and Kalafatis, S. (2022). Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0269401"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Arvind, C.S., Jyothi, R., Kaushal, K., Girish, G., Saurav, R., and Chetankumar, G. (2020, January 1\u20134). Edge computing based smart aquaponics monitoring system using deep learning in IoT environment. Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia.","DOI":"10.1109\/SSCI47803.2020.9308395"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dhal, S.B., Mahanta, S., Gumero, J., O\u2019Sullivan, N., Soetan, M., Louis, J., Gadepally, K.C., Mahanta, S., Lusher, J., and Kalafatis, S. (2023). An IoT-based data-driven real-time monitoring system for control of heavy metals to ensure optimal lettuce growth in hydroponic set-ups. Sensors, 23.","DOI":"10.3390\/s23010451"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Habib, M.R., Mahanta, S., Jolly, Y.N., and Moore, J.M. (2022). Alleviating heavy metal toxicity in milk and water through a synergistic approach of absorption technique and high voltage atmospheric cold plasma and probable rheological changes. Biomolecules, 12.","DOI":"10.3390\/biom12070913"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2122","DOI":"10.1111\/ijfs.16979","article-title":"Pulsed electric field processing in the dairy sector: A review of applications, quality impact and implementation challenges","volume":"59","author":"Vashisht","year":"2024","journal-title":"Int. J. Food Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vashisht, P., Verma, D., Charles, A.P.R., Saini, G.S., Sharma, S., Singh, L., Mahanta, S., Mahanta, S., Singh, K., and Gaurav, G. (2023). Ozone processing in the dairy sector: A review of applications, quality impact and implementation challenges. ChemRxiv.","DOI":"10.26434\/chemrxiv-2023-m3csm"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dhal, S.B., Mahanta, S., Gadepally, K.C., He, S., Hughes, M., Moore, J., Nowka, K.J., and Kalafatis, S. (2023, January 1\u201316). CNN-based real-time prediction of growth stage in soybeans cultivated in hydroponic set-ups. Proceedings of the SoutheastCon 2023, Orlando, FL, USA.","DOI":"10.1109\/SoutheastCon51012.2023.10115131"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/S1161-0301(02)00107-7","article-title":"The DSSAT cropping system model","volume":"18","author":"Jones","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/S1161-0301(02)00100-4","article-title":"Development of a generic crop model template in the cropping system model APSIM","volume":"18","author":"Wang","year":"2002","journal-title":"Eur. J. Agron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ullah, M., Ullah, H., Khan, S.D., and Cheikh, F.A. (2019, January 28\u201331). Stacked lstm network for human activity recognition using smartphone data. Proceedings of the 2019 8th European Workshop on Visual Information Processing (EUVIP), Roma, Italy.","DOI":"10.1109\/EUVIP47703.2019.8946180"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Graves, A., Jaitly, N., and Mohamed, A.R. (2013, January 8\u201312). Hybrid speech recognition with deep bidirectional LSTM. Proceedings of the 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Czech Republic.","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"ref_18","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_19","unstructured":"Zhao, Y., Xue, J., and Chen, X. (2014). Speech and Audio Processing for Coding, Enhancement and Recognition, Springer."},{"key":"ref_20","unstructured":"Jiang, Z., Liu, C., Hendricks, N.P., Ganapathysubramanian, B., Hayes, D.J., and Sarkar, S. (2018). Predicting county level corn yields using deep long short term memory models. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115511","DOI":"10.1016\/j.eswa.2021.115511","article-title":"DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting","volume":"184","author":"Gavahi","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop yield prediction using machine learning: A systematic literature review","volume":"177","author":"Kassahun","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yuan, C.Z., and Ling, S.K. (2020, January 12\u201314). Long short-term memory model based agriculture commodity price prediction application. Proceedings of the 2020 2nd International Conference on Information Technology and Computer Communications, Kuala Lumpur, Malaysia.","DOI":"10.1145\/3417473.3417481"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105612","DOI":"10.1016\/j.compag.2020.105612","article-title":"Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation","volume":"176","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xiao, Q., Li, W., Kai, Y., Chen, P., Zhang, J., and Wang, B. (2019). Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-3262-y"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s12355-011-0071-7","article-title":"Forecasting sugarcane yield of Tamilnadu using ARIMA models","volume":"13","author":"Suresh","year":"2011","journal-title":"Sugar Tech"},{"key":"ref_27","first-page":"810","article-title":"Using a dynamic time series model (ARIMA) for forecasting of Egyptian cotton crop variables","volume":"31","author":"Elsamie","year":"2021","journal-title":"J. Anim. Plant Sci."},{"key":"ref_28","first-page":"5504","article-title":"Forecasting of cotton production in India using ARIMA model","volume":"2320","author":"Poyyamozhi","year":"2016","journal-title":"Asia Pac. J. Res. ISSN (Print)"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.eja.2006.12.001","article-title":"Canopy reflectance in cotton for growth assessment and lint yield prediction","volume":"26","author":"Zhao","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ballester, C., Hornbuckle, J., Brinkhoff, J., Smith, J., and Quayle, W. (2017). Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery. Remote Sens., 9.","DOI":"10.3390\/rs9111149"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105942","DOI":"10.1016\/j.compag.2020.105942","article-title":"Introducing digital twins to agriculture","volume":"184","author":"Pylianidis","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e20058","DOI":"10.1002\/ppj2.20058","article-title":"Unmanned aerial system-based high-throughput phenotyping for plant breeding","volume":"6","author":"Bhandari","year":"2023","journal-title":"Plant Phenome J."},{"key":"ref_33","first-page":"1674","article-title":"Using smartphone technology to assess field crop stands","volume":"108","author":"Sullivan","year":"2016","journal-title":"Agron. J."},{"key":"ref_34","first-page":"2296","article-title":"Better acf and pacf plots, but no optimal linear prediction","volume":"8","author":"Hyndman","year":"2014","journal-title":"Electron. J. Stat. [E]"},{"key":"ref_35","first-page":"214","article-title":"Forecasting the population of Pakistan using ARIMA models","volume":"46","author":"Zakria","year":"2009","journal-title":"Pak. J. Agric. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1023\/A:1018981505752","article-title":"Polynomial interpolation in several variables","volume":"12","author":"Gasca","year":"2000","journal-title":"Adv. Comput. Math."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107107","DOI":"10.1016\/j.compag.2022.107107","article-title":"Predicting and interpreting cotton yield and its determinants under long-term conservation management practices using machine learning","volume":"199","author":"Dhaliwal","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","first-page":"144","article-title":"Cotton yield prediction using artificial neural networks","volume":"7","author":"Chen","year":"2014","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_39","first-page":"4653","article-title":"Monitoring cotton yield and growth anomalies using AVHRR NDVI time-series data","volume":"23","author":"Zhang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Risal, A., Niu, H., Landivar-Scott, J.L., Maeda, M.M., Bednarz, C.W., Landivar-Bowles, J., Duffield, N., Payton, P., Pal, P., and Lascano, R.J. (2024). Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains. Water, 16.","DOI":"10.3390\/w16091300"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1214\/aos\/1069362747","article-title":"Alignment of curves by dynamic time warping","volume":"25","author":"Wang","year":"1997","journal-title":"Ann. Stat."},{"key":"ref_42","unstructured":"Berndt Donald, J., and Clifford, J. (August, January 31). Using dynamic time warping to find patterns in time series. Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"561","DOI":"10.3233\/IDA-2007-11508","article-title":"Toward accurate dynamic time warping in linear time and space","volume":"11","author":"Stan","year":"2007","journal-title":"Intell. Data Anal."},{"key":"ref_44","unstructured":"Gibran, K., and Bushrui, S.B. (2012). The Prophet: A New Annotated Edition, Simon and Schuster."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1016\/j.matcom.2012.01.001","article-title":"From general state-space to VARMAX models","volume":"82","author":"Casals","year":"2012","journal-title":"Math. Comput. Simul."},{"key":"ref_46","unstructured":"Aoki, M. (2013). State Space Modeling of Time Series, Springer Science & Business Media."},{"key":"ref_47","unstructured":"Rangapuram, S.S., Seeger, M.W., Gasthaus, J., Stella, L., Wang, Y., and Januschowski, T. (2018). Advances in Neural Information Processing Systems, Curran Associates Inc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1906\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:48:47Z","timestamp":1760107727000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1906"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,25]]},"references-count":47,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111906"],"URL":"https:\/\/doi.org\/10.3390\/rs16111906","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,25]]}}}