Exploring the transferability of wheat nitrogen status estimation with multisource data and Evolutionary Algorithm-Deep Learning (EA-DL) framework

[1]  Yuanjie Zheng,et al.  BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment , 2022, Plant phenomics.

[2]  Y. Miao,et al.  Improving Estimation of Winter Wheat Nitrogen Status Using Random Forest by Integrating Multi-Source Data Across Different Agro-Ecological Zones , 2022, Frontiers in Plant Science.

[3]  Guijun Yang,et al.  Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data , 2022, Remote Sensing of Environment.

[4]  Syed Tahir Ata-UI-Karim,et al.  Improving wheat yield prediction integrating proximal sensing and weather data with machine learning , 2022, Comput. Electron. Agric..

[5]  Fei Li,et al.  Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV , 2022, Remote. Sens..

[6]  Jingfeng Huang,et al.  Large-Scale Rice Mapping Using Multi-Task Spatiotemporal Deep Learning and Sentinel-1 SAR Time Series , 2022, Remote. Sens..

[7]  C. J. Ransom,et al.  Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning , 2022, Remote. Sens..

[8]  Y. Miao,et al.  Combining leaf fluorescence and active canopy reflectance sensing technologies to diagnose maize nitrogen status across growth stages , 2022, Precision Agriculture.

[9]  Liying Chang,et al.  Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon , 2021, Horticulturae.

[10]  Yan Liu,et al.  Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors , 2021, Remote. Sens..

[11]  Chongyang Wang,et al.  Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery , 2021, Remote. Sens..

[12]  Guiping Hu,et al.  A genetic algorithm-assisted deep learning approach for crop yield prediction , 2021, Soft Computing.

[13]  Tan Liu,et al.  A method combining ELM and PLSR (ELM-P) for estimating chlorophyll content in rice with feature bands extracted by an improved ant colony optimization algorithm , 2021, Comput. Electron. Agric..

[14]  Lin-zhang Yang,et al.  Effect of fertilization on nitrogen losses through surface runoffs in Chinese farmlands: A meta-analysis. , 2021, The Science of the total environment.

[15]  Ahmet Kara,et al.  Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm , 2021, Expert Syst. Appl..

[16]  Baojing Gu,et al.  Optimizing nitrogen fertilizer use for more grain and less pollution , 2021, Journal of Cleaner Production.

[17]  J. Palta,et al.  Wheat cultivars with small root length density in the topsoil increased post-anthesis water use and grain yield in the semi-arid region on the Loess Plateau , 2021 .

[18]  Ruiliang Pu,et al.  An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives , 2021 .

[19]  F. Tao,et al.  Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine , 2021 .

[20]  Sumit Singh Chauhan,et al.  A review on genetic algorithm: past, present, and future , 2020, Multim. Tools Appl..

[21]  Zhenhai Li,et al.  Progress of hyperspectral data processing and modelling for cereal crop nitrogen monitoring , 2020, Comput. Electron. Agric..

[22]  Y. Ying,et al.  DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation , 2020, Environmental Research Letters.

[23]  Haikuan Feng,et al.  Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index , 2020 .

[24]  Tiantian Wang,et al.  Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning , 2020, Remote. Sens..

[25]  S. Khaki,et al.  A CNN-RNN Framework for Crop Yield Prediction , 2019, Frontiers in Plant Science.

[26]  Huichun Ye,et al.  In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages , 2019, Remote. Sens..

[27]  W. Cao,et al.  Response of biomass accumulation in wheat to low-temperature stress at jointing and booting stages , 2019, Environmental and Experimental Botany.

[28]  Xianfu Chen,et al.  Deep Learning with Long Short-Term Memory for Time Series Prediction , 2018, IEEE Communications Magazine.

[29]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[30]  E. Davidson,et al.  Managing nitrogen for sustainable development , 2015, Nature.

[31]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[32]  Y. Zhu,et al.  Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[33]  Susan L. Ustin,et al.  Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices , 2014 .

[34]  Earl D. Vories,et al.  Corn response to nitrogen is influenced by soil texture and weather , 2012 .

[35]  Fusuo Zhang,et al.  Critical Nitrogen Dilution Curve for Optimizing Nitrogen Management of Winter Wheat Production in the North China Plain , 2012 .

[36]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[37]  Zhenwang Li,et al.  Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms , 2021, Comput. Electron. Agric..

[38]  Fei Li,et al.  Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression , 2014 .

[39]  I. Khaliq,et al.  EFFECT OF WATER STRESS ON PHYSICO-CHEMICAL PROPERTIES OF WHEAT (TRITICUM AESTIVUM L.) , 2009 .