Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

[1] Luo SJ(2018)Analysis of hyperspectral variation of different potato cultivars based on continuum removed spectra Spectrosc Spec Anal 38 3231-3237

[2] He YB(2017)Molecular and biochemical characterization of a potato collection with contrasting tuber carotenoid content PLoS ONE 12 e0184143-112

[3] Duan DD(2017)Ramirez DA Linking process-based potato models with light reflectance data: does model complexity enhance yield prediction accuracy? Eur J Agron 82 104-3220

[4] Wang ZZ(2018)Analysis on the ability of distinguishing potato varieties with different hyperspectral parameters Spectrosc Spec Anal 38 3215-3508

[5] Zhang JK(2016)Prediction of potato crop yield using precision agriculture techniques PLoS ONE 11 e0162219-511

[6] Zhang YT(2000)Estimating crop yields and production by integrating the FAO crop specific water balance model with real-time satellite data and ground-based ancillary data Int J Remote Sens 21 3487-185

[7] Sulli M(2018)Water productivity and crop yield: a simplified remote sensing driven operational approach Agric Forest Meteorol 249 501-8124

[8] Mandolino G(2014)Potato, sweet potato, and yam models for climate change: a review Field Crops Res 166 173-168

[9] Sturaro M(2019)Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries Int J Remote Sens 40 8093-106

[10] Onofri C(2019)Assimilation of sentinel-2 leaf area index data into a physically-based crop growth model for yield estimation Agronomy. 9 255-681

[1] Luo SJ(2018)Analysis of hyperspectral variation of different potato cultivars based on continuum removed spectra Spectrosc Spec Anal 38 3231-3237

[2] He YB(2017)Molecular and biochemical characterization of a potato collection with contrasting tuber carotenoid content PLoS ONE 12 e0184143-112

[3] Duan DD(2017)Ramirez DA Linking process-based potato models with light reflectance data: does model complexity enhance yield prediction accuracy? Eur J Agron 82 104-3220

[4] Wang ZZ(2018)Analysis on the ability of distinguishing potato varieties with different hyperspectral parameters Spectrosc Spec Anal 38 3215-3508

[5] Zhang JK(2016)Prediction of potato crop yield using precision agriculture techniques PLoS ONE 11 e0162219-511

[6] Zhang YT(2000)Estimating crop yields and production by integrating the FAO crop specific water balance model with real-time satellite data and ground-based ancillary data Int J Remote Sens 21 3487-185

[7] Sulli M(2018)Water productivity and crop yield: a simplified remote sensing driven operational approach Agric Forest Meteorol 249 501-8124

[8] Mandolino G(2014)Potato, sweet potato, and yam models for climate change: a review Field Crops Res 166 173-168

[9] Sturaro M(2019)Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries Int J Remote Sens 40 8093-106

[10] Onofri C(2019)Assimilation of sentinel-2 leaf area index data into a physically-based crop growth model for yield estimation Agronomy. 9 255-681