Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques
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Lucas Prado Osco | José Marcato Junior | Wesley Nunes Gonçalves | Hemerson Pistori | Carlos Antonio da Silva Junior | Ana Paula Marques Ramos | Paulo Eduardo Teodoro | Danielle Elis Garcia Furuya | Dthenifer Cordeiro Santana | Larissa Pereira Ribeiro Teodoro | Fábio Henrique Rojo Baio | Danielle Elis Garcia Furuya | Dthenifer Cordeiro Santana | W. Gonçalves | P. Teodoro | J. M. Junior | H. Pistori | L. Osco | A. P. Ramos | F. Baio | L. P. Teodoro | D. C. Santana
[1] Jakub Nalepa,et al. Selecting training sets for support vector machines: a review , 2018, Artificial Intelligence Review.
[2] Ashutosh Kumar Singh,et al. Machine Learning for High-Throughput Stress Phenotyping in Plants. , 2016, Trends in plant science.
[3] Shaokun Li,et al. Traits of plant morphology, stalk mechanical strength, and biomass accumulation in the selection of lodging-resistant maize cultivars , 2020 .
[4] Douglas G. Goodin,et al. Hyperspectral Analysis of Leaf Pigments and Nutritional Elements in Tallgrass Prairie Vegetation , 2019, Front. Plant Sci..
[5] P. Miphokasap,et al. Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery , 2018 .
[6] Eija Honkavaara,et al. A Novel Deep Learning Method to Identify Single Tree Species in UAV-Based Hyperspectral Images , 2020, Remote. Sens..
[7] Andrew K. Skidmore,et al. Spatially-explicit modelling with support of hyperspectral data can improve prediction of plant traits , 2019, Remote Sensing of Environment.
[8] Na Liu,et al. Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards , 2017, Sensors.
[9] Zdeněk Buk,et al. Novel age estimation model based on development of permanent teeth compared with classical approach and other modern data mining methods. , 2017, Forensic science international.
[10] Fan Li,et al. Estimation of nitrogen and carbon content from soybean leaf reflectance spectra using wavelet analysis under shade stress , 2019, Comput. Electron. Agric..
[11] A. Arabameri,et al. Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India. , 2020, The Science of the total environment.
[12] Jonathan Li,et al. A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements , 2020, Remote. Sens..
[13] H. Xie,et al. Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.
[14] Wei Li,et al. A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle , 2018, Remote. Sens..
[15] Vijay Kumar,et al. Counting Apples and Oranges With Deep Learning: A Data-Driven Approach , 2017, IEEE Robotics and Automation Letters.
[16] Najat Ali,et al. Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets , 2019, SN Applied Sciences.
[17] J. Viana,et al. Genetic diversity and path analysis for nitrogen use efficiency of tropical popcorn (Zea mays ssp. everta ) inbred lines in adult stage , 2018, Plant Breeding.
[18] Danfeng Huang,et al. Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L. , 2019, Sensors.
[19] H. Fathizad,et al. Comparison of RBF and MLP neural network performance and regression analysis to estimate carbon sequestration , 2020, International Journal of Environmental Science and Technology.
[20] Hang Zhou,et al. Deep learning based multi-temporal crop classification , 2019, Remote Sensing of Environment.
[21] Tianyi Wang,et al. Automatic Classification of Cotton Root Rot Disease Based on UAV Remote Sensing , 2020, Remote. Sens..
[22] Xiaodong Yang,et al. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data , 2019, Plant Methods.
[23] Jie Sun,et al. County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model , 2019, Sensors.
[24] Zhou Lei,et al. An Evaluation of Spaceborne Imaging Spectrometry for Estimation of Forest Canopy Nitrogen Concentration in a Subtropical Conifer Plantation of Southern China , 2014 .
[25] David B. Lobell,et al. Smallholder maize area and yield mapping at national scales with Google Earth Engine , 2019, Remote Sensing of Environment.
[26] Nilton Nobuhiro Imai,et al. Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[27] Pieter Badenhorst,et al. Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program , 2019, Remote. Sens..
[28] James Brinkhoff,et al. Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data , 2019, Remote. Sens..
[29] Tiantian Wang,et al. Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning , 2020, Remote. Sens..
[30] David B. Lobell,et al. Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques , 2019, Remote Sensing of Environment.
[31] C. Daughtry,et al. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? , 2018 .
[32] Li He,et al. Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data , 2016 .
[33] Rolf Becker,et al. Relationship between Remote Sensing Data, Plant Biomass and Soil Nitrogen Dynamics in Intensively Managed Grasslands under Controlled Conditions , 2017, Sensors.
[34] Li Lin,et al. Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm , 2018, Remote. Sens..
[35] Huihui Yu,et al. Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review , 2020, Sensors.
[36] Salah Sukkarieh,et al. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review , 2018, Comput. Electron. Agric..
[37] Chris Brien,et al. The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum) , 2019, Front. Plant Sci..
[38] A. Gitelson,et al. Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .
[39] Jianlong Li,et al. Prediction of cadmium concentration in brown rice before harvest by hyperspectral remote sensing , 2018, Environmental Science and Pollution Research.
[40] H. Feng,et al. Simulation of plant height of winter wheat under soil Water stress using modified growth functions , 2020 .
[41] C. F. Azevedo,et al. Multi-trait multi-environment Bayesian model reveals G x E interaction for nitrogen use efficiency components in tropical maize , 2018, PloS one.
[42] Dehai Zhu,et al. Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids , 2019, Remote. Sens..
[43] Brian G. Leib,et al. Prediction of cotton lint yield from phenology of crop indices using artificial neural networks , 2018, Comput. Electron. Agric..
[44] Jonathan Li,et al. Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery , 2019, Remote. Sens..
[45] Jonathan Li,et al. Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks , 2019, Remote. Sens..
[46] Terry Griffin,et al. Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques , 2018, Remote. Sens..
[47] Pierre Defourny,et al. Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems , 2018, Remote Sensing of Environment.
[48] Yang Song,et al. Soybean canopy nitrogen monitoring and prediction using ground based multispectral remote sensors , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[49] Armin B. Cremers,et al. In-field cotton detection via region-based semantic image segmentation , 2016, Comput. Electron. Agric..
[50] Derek T. Anderson,et al. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community , 2017 .
[51] Katja Berger,et al. Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions. , 2020, Remote sensing of environment.
[52] George Alan Blackburn,et al. High resolution wheat yield mapping using Sentinel-2 , 2019, Remote Sensing of Environment.
[53] Honggang Bu,et al. Use of corn height measured with an acoustic sensor improves yield estimation with ground based active optical sensors , 2016, Comput. Electron. Agric..
[54] Elfatih M. Abdel-Rahman,et al. Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data , 2013 .
[55] M. Weiss,et al. Remote sensing for agricultural applications: A meta-review , 2020 .
[56] J. J. Varco,et al. Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status , 2014, Precision Agriculture.
[57] Timothy A. Warner,et al. Implementation of machine-learning classification in remote sensing: an applied review , 2018 .
[58] Luis Guanter,et al. Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations , 2019, Remote Sensing of Environment.
[59] Stefano Amaducci,et al. Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery , 2014, Remote. Sens..
[60] A. Huete. A soil-adjusted vegetation index (SAVI) , 1988 .
[61] M. Kelly,et al. Remotely-Sensed Indicators of N-Related Biomass Allocation in Schoenoplectus acutus , 2014, PloS one.
[62] Heather McNairn,et al. International Journal of Applied Earth Observation and Geoinformation , 2014 .
[63] Luis Miguel Contreras-Medina,et al. A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances , 2013, Sensors.
[64] Ruben Van De Kerchove,et al. Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data , 2015, Int. J. Appl. Earth Obs. Geoinformation.
[65] Bruno Basso,et al. Assessing the Robustness of Vegetation Indices to Estimate Wheat N in Mediterranean Environments , 2014, Remote. Sens..
[66] A. Gitelson,et al. Quantitative estimation of chlorophyll-a using reflectance spectra : experiments with autumn chestnut and maple leaves , 1994 .
[67] Mariana Belgiu,et al. Random forest in remote sensing: A review of applications and future directions , 2016 .