{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:57:54Z","timestamp":1770890274768,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T00:00:00Z","timestamp":1708732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42101362"],"award-info":[{"award-number":["42101362"]}]},{"name":"National Natural Science Foundation of China","award":["32271993"],"award-info":[{"award-number":["32271993"]}]},{"name":"National Natural Science Foundation of China","award":["42371373"],"award-info":[{"award-number":["42371373"]}]},{"name":"National Natural Science Foundation of China","award":["232102111123"],"award-info":[{"award-number":["232102111123"]}]},{"name":"National Natural Science Foundation of China","award":["222103810024"],"award-info":[{"award-number":["222103810024"]}]},{"name":"National Natural Science Foundation of China","award":["222301420114"],"award-info":[{"award-number":["222301420114"]}]},{"name":"Henan Province Science and Technology Research Project","award":["42101362"],"award-info":[{"award-number":["42101362"]}]},{"name":"Henan Province Science and Technology Research Project","award":["32271993"],"award-info":[{"award-number":["32271993"]}]},{"name":"Henan Province Science and Technology Research Project","award":["42371373"],"award-info":[{"award-number":["42371373"]}]},{"name":"Henan Province Science and Technology Research Project","award":["232102111123"],"award-info":[{"award-number":["232102111123"]}]},{"name":"Henan Province Science and Technology Research Project","award":["222103810024"],"award-info":[{"award-number":["222103810024"]}]},{"name":"Henan Province Science and Technology Research Project","award":["222301420114"],"award-info":[{"award-number":["222301420114"]}]},{"name":"Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province","award":["42101362"],"award-info":[{"award-number":["42101362"]}]},{"name":"Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province","award":["32271993"],"award-info":[{"award-number":["32271993"]}]},{"name":"Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province","award":["42371373"],"award-info":[{"award-number":["42371373"]}]},{"name":"Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province","award":["232102111123"],"award-info":[{"award-number":["232102111123"]}]},{"name":"Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province","award":["222103810024"],"award-info":[{"award-number":["222103810024"]}]},{"name":"Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province","award":["222301420114"],"award-info":[{"award-number":["222301420114"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5\u2013P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625\u20130.9875; user\u2019s accuracy: 0.9583\u20130.9933; and producer\u2019s accuracy: 0.9634\u20131). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5\u2013P7 (OA: 0.9125\u20130.9750; UA: 0.878\u20130.9778; and PA: 0.9362\u20130.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.<\/jats:p>","DOI":"10.3390\/rs16050784","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T10:40:17Z","timestamp":1708944017000},"page":"784","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation"],"prefix":"10.3390","volume":"16","author":[{"given":"Jingyu","family":"Hu","sequence":"first","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Hao","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Qilei","family":"Wang","sequence":"additional","affiliation":[{"name":"Henan Jinyuan Seed Industry Co., Ltd., Zhengzhou 450003, China"}]},{"given":"Jianing","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"}]},{"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8506-7295","authenticated-orcid":false,"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8263-0222","authenticated-orcid":false,"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Hongbo","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Qinglin","family":"Niu","sequence":"additional","affiliation":[{"name":"Farmland Irrigation Research Institute (FIRI), Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"},{"name":"Institute of Quantitative Remote Sensing and Smart Agriculture, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9766-5313","authenticated-orcid":false,"given":"Jibo","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108049","DOI":"10.1016\/j.compag.2023.108049","article-title":"Navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network","volume":"212","author":"Diao","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22885","DOI":"10.1038\/s41598-023-50129-w","article-title":"Tomato maturity recognition with convolutional transformers","volume":"13","author":"Khan","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107551","DOI":"10.1016\/j.compag.2022.107551","article-title":"Detecting volunteer cotton plants in a corn field with deep learning on UAV remote-sensing imagery","volume":"204","author":"Hardin","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7939","DOI":"10.1038\/s41467-023-42991-z","article-title":"Soybean reduced internode 1 determines internode length and improves grain yield at dense planting","volume":"14","author":"Li","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112408","DOI":"10.1016\/j.rse.2021.112408","article-title":"Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach","volume":"259","author":"Ma","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108011","DOI":"10.1016\/j.compag.2023.108011","article-title":"Hyperspectral-to-image transform and CNN transfer learning enhancing soybean LCC estimation","volume":"211","author":"Yue","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"0101","DOI":"10.34133\/remotesensing.0101","article-title":"Mapping Spatially Seamless Fractional Vegetation Cover over China at a 30-m Resolution and Semimonthly Intervals in 2010\u20132020 Based on Google Earth Engine","volume":"3","author":"Zhao","year":"2023","journal-title":"J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13007-023-00982-7","article-title":"Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm","volume":"19","author":"Pan","year":"2023","journal-title":"Plant Methods"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s13007-021-00752-3","article-title":"Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing","volume":"17","author":"Yue","year":"2021","journal-title":"Plant Methods"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108421","DOI":"10.1016\/j.compag.2023.108421","article-title":"Mapping cropland rice residue cover using a radiative transfer model and deep learning","volume":"215","author":"Yue","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vahidi, M., Shafian, S., Thomas, S., and Maguire, R. (2023). Pasture Biomass Estimation Using Ultra-High-Resolution RGB UAVs Images and Deep Learning. Remote Sens., 15.","DOI":"10.3390\/rs15245714"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108260","DOI":"10.1016\/j.compag.2023.108260","article-title":"Improved potato AGB estimates based on UAV RGB and hyperspectral images","volume":"214","author":"Liu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pan, D., Li, C., Yang, G., Ren, P., Ma, Y., Chen, W., Feng, H., Chen, R., Chen, X., and Li, H. (2023). Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images. Remote Sens., 15.","DOI":"10.3390\/rs15225413"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, Y., Hao, Z., Guo, Z., Liu, Z., and Huang, J. (2023). Detection and Mapping of Chestnut Using Deep Learning from High-Resolution UAV-Based RGB Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15204923"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108144","DOI":"10.1016\/j.compag.2023.108144","article-title":"High-quality images and data augmentation based on inverse projection transformation significantly improve the estimation accuracy of biomass and leaf area index","volume":"212","author":"Che","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"938216","DOI":"10.3389\/fpls.2022.938216","article-title":"Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height","volume":"13","author":"Liu","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9802585","DOI":"10.34133\/2022\/9802585","article-title":"Application of UAV multisensor data and ensemble approach for high-throughput estimation of maize phenotyping traits","volume":"2022","author":"Shu","year":"2022","journal-title":"Plant Phenomics"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11240-009-9635-6","article-title":"Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis","volume":"100","author":"Yadav","year":"2010","journal-title":"Plant Cell Tissue Organ Cult. (PCTOC)"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107627","DOI":"10.1016\/j.compag.2023.107627","article-title":"Estimating vertically growing crop above-ground biomass based on UAV remote sensing","volume":"205","author":"Yue","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"402","DOI":"10.3389\/fpls.2020.00402","article-title":"A monitoring system for the segmentation and grading of broccoli head based on deep learning and neural networks","volume":"11","author":"Zhou","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113313","DOI":"10.1016\/j.rse.2022.113313","article-title":"Sensitivity of solar-induced fluorescence to spectral stray light in high resolution imaging spectroscopy","volume":"285","author":"Albert","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.agrformet.2012.06.009","article-title":"Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis","volume":"165","author":"Cong","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2017.02.001","article-title":"Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm","volume":"126","author":"Jin","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xie, J., Wang, J., Chen, Y., Gao, P., Yin, H., Chen, S., Sun, D., Wang, W., Mo, H., and Shen, J. (2023). Estimating the SPAD of Litchi in the Growth Period and Autumn Shoot Period Based on UAV Multi-Spectrum. Remote Sens., 15.","DOI":"10.3390\/rs15245767"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"De Souza, R., Pe\u00f1a-Fleitas, M.T., Thompson, R.B., Gallardo, M., and Padilla, F.M. (2020). Assessing performance of vegetation indices to estimate nitrogen nutrition index in pepper. Remote Sens., 12.","DOI":"10.3390\/rs12050763"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1012070","DOI":"10.3389\/fpls.2022.1012070","article-title":"Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices","volume":"13","author":"Fan","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, X., Wu, J., Luo, W., Tian, L., Wang, Y., Liu, Y., Zhang, L., Zhao, C., and Zhang, W. (2023). Soil Moisture Monitoring and Evaluation in Agricultural Fields Based on NDVI Long Time Series and CEEMDAN. Remote Sens., 15.","DOI":"10.3390\/rs15205008"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108169","DOI":"10.1016\/j.compag.2023.108169","article-title":"Leaf area index estimation under wheat powdery mildew stress by integrating UAV-based spectral, textural and structural features","volume":"213","author":"Liu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108147","DOI":"10.1016\/j.compag.2023.108147","article-title":"Using an optimized texture index to monitor the nitrogen content of potato plants over multiple growth stages","volume":"212","author":"Fan","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, W., Wang, J., Zhang, Y., Yin, Q., Wang, W., Zhou, G., and Huo, Z. (2023). Combining Texture, Color, and Vegetation Index from Unmanned Aerial Vehicle Multispectral Images to Estimate Winter Wheat Leaf Area Index during the Vegetative Growth Stage. Remote Sens., 15.","DOI":"10.3390\/rs15245715"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108229","DOI":"10.1016\/j.compag.2023.108229","article-title":"Estimating potato above-ground biomass by using integrated unmanned aerial system-based optical, structural, and textural canopy measurements","volume":"213","author":"Liu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5704154","DOI":"10.34133\/2019\/5704154","article-title":"Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image","volume":"2019","author":"Sun","year":"2019","journal-title":"Plant Phenomics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/s13007-021-00809-3","article-title":"Cotton stubble detection based on wavelet decomposition and texture features","volume":"17","author":"Yang","year":"2021","journal-title":"Plant Methods"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"113710","DOI":"10.1016\/j.rse.2023.113710","article-title":"Discriminative feature constraints via supervised contrastive learning for few-shot forest tree species classification using airborne hyperspectral images","volume":"295","author":"Chen","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jjagwe, P., Chandel, A.K., and Langston, D. (2023). Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques. Land, 12.","DOI":"10.3390\/land12122188"},{"key":"ref_36","first-page":"102890","article-title":"Comparison of RFE-DL and stacking ensemble learning algorithms for classifying mangrove species on UAV multispectral images","volume":"112","author":"Fu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"447","DOI":"10.5424\/sjar\/2013112-3645","article-title":"Visual definition of physiological maturity in sunflower (Helianthus annuus L.) is associated with receptacle quantitative color parameters","volume":"11","author":"Hernandez","year":"2013","journal-title":"Span. J. Agric. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"679","DOI":"10.4141\/CJPS07058","article-title":"Evolution of kernels moisture content and physiological maturity determination of corn (Zea mays L.)","volume":"88","author":"Tremblay","year":"2008","journal-title":"Can. J. Plant Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.fcr.2016.01.002","article-title":"Cotton crop maturity: A compendium of measures and predictors","volume":"191","author":"Gwathmey","year":"2016","journal-title":"Field Crops Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hu, J., Yue, J., Xu, X., Han, S., Sun, T., Liu, Y., Feng, H., and Qiao, H. (2023). UAV-Based Remote Sensing for Soybean FVC, LCC, and Maturity Monitoring. Agriculture, 13.","DOI":"10.3390\/agriculture13030692"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, H., Ding, X., Cao, X., Chen, H., and Zhang, S. (2023). Estimating Maize Maturity by Using UAV Multi-Spectral Images Combined with a CCC-Based Model. Drones, 7.","DOI":"10.3390\/drones7090586"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhou, P., Lin, Y., Sun, S., Zhang, H., Xu, W., and Yang, S. (2022). Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph192416544"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s00425-022-03867-6","article-title":"Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models","volume":"255","author":"Buchaillot","year":"2022","journal-title":"Planta"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhang, W., Deng, H., Dong, L., Zhang, H., Tang, L., Zheng, Y., and Zhao, Z. (2023). A Combination of OBIA and Random Forest Based on Visible UAV Remote Sensing for Accurately Extracted Information about Weeds in Areas with Different Weed Densities in Farmland. Remote Sens., 15.","DOI":"10.3390\/rs15194696"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2023.03.003","article-title":"Reconstructing cloud-contaminated NDVI images with SAR-Optical fusion using spatio-temporal partitioning and multiple linear regression","volume":"198","author":"Mao","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Uribeetxebarria, A., Castell\u00f3n, A., and Aizpurua, A. (2023). Optimizing Wheat Yield Prediction Integrating Data from Sentinel-1 and Sentinel-2 with CatBoost Algorithm. Remote Sens., 15.","DOI":"10.3390\/rs15061640"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107537","DOI":"10.1016\/j.compag.2022.107537","article-title":"Retrieving soil moisture from grape growing areas using multi-feature and stacking-based ensemble learning modeling","volume":"204","author":"Tao","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"107621","DOI":"10.1016\/j.compag.2023.107621","article-title":"Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass","volume":"205","author":"Derraz","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/BF00531558","article-title":"On the maximum deviation between the histogram and the underlying density","volume":"58","author":"Freedman","year":"1981","journal-title":"Z. F\u00fcr Wahrscheinlichkeitstheorie Und Verwandte Geb."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13007-018-0281-z","article-title":"A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons","volume":"14","author":"Lu","year":"2018","journal-title":"Plant Methods"},{"key":"ref_51","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_52","unstructured":"Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T. (2000, January 16\u201319). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/j.rse.2010.01.021","article-title":"The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India","volume":"114","author":"Dash","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves","volume":"48","author":"Gamon","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/j.rse.2007.07.010","article-title":"A VARI-based relative greenness from MODIS data for computing the Fire Potential Index","volume":"112","author":"Schneider","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Huang, Y., Wen, X., Gao, Y., Zhang, Y., and Lin, G. (2023). Tree Species Classification in UAV Remote Sensing Images Based on Super-Resolution Reconstruction and Deep Learning. Remote Sens., 15.","DOI":"10.3390\/rs15112942"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"106603","DOI":"10.1016\/j.compag.2021.106603","article-title":"Improving estimation of LAI dynamic by fusion of morphological and vegetation indices based on UAV imagery","volume":"192","author":"Qiao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2019.02.022","article-title":"Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices","volume":"150","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Dericquebourg, E., Hafiane, A., and Canals, R. (2022). Generative-Model-Based Data Labeling for Deep Network Regression: Application to Seed Maturity Estimation from UAV Multispectral Images. Remote Sens., 14.","DOI":"10.3390\/rs14205238"},{"key":"ref_62","first-page":"100233","article-title":"An applied deep learning approach for estimating soybean relative maturity from UAV imagery to aid plant breeding decisions","volume":"7","author":"Moeinizade","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"107723","DOI":"10.1016\/j.compag.2023.107723","article-title":"Leaf area index estimation of pergola-trained vineyards in arid regions using classical and deep learning methods based on UAV-based RGB images","volume":"207","author":"Ilniyaz","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_64","first-page":"5514610","article-title":"Enhanced Leaf Area Index Estimation with CROP-DualGAN Network","volume":"61","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Yamaguchi, T., Tanaka, Y., Imachi, Y., Yamashita, M., and Katsura, K. (2020). Feasibility of combining deep learning and RGB images obtained by unmanned aerial vehicle for leaf area index estimation in rice. Remote Sens., 13.","DOI":"10.3390\/rs13010084"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s11119-022-09932-0","article-title":"Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: A comparison with traditional machine learning algorithms","volume":"24","author":"Yu","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Wu, M., Dou, S., Lin, N., Jiang, R., and Zhu, B. (2023). Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images. Remote Sens., 15.","DOI":"10.3390\/rs15194713"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fu, B., Sun, X., Yao, H., Zhang, S., Wu, Y., Kuang, H., and Deng, T. (2023). Effects of Multi-Growth Periods UAV Images on Classifying Karst Wetland Vegetation Communities Using Object-Based Optimization Stacking Algorithm. Remote Sens., 15.","DOI":"10.3390\/rs15164003"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wang, L., Gao, R., Li, C., Wang, J., Liu, Y., Hu, J., Li, B., Qiao, H., Feng, H., and Yue, J. (2023). Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images. Remote Sens., 15.","DOI":"10.3390\/rs15194807"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/784\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:04:16Z","timestamp":1760105056000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/784"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,24]]},"references-count":69,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050784"],"URL":"https:\/\/doi.org\/10.3390\/rs16050784","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,24]]}}}