Potato Crop Analysis, Production, Detection, Health, Change

Chapter Contents (Back)
Classification. Potato. Include other root crops.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

Franceschini, M.H.D.[Marston Héracles Domingues], Bartholomeus, H.[Harm], van Apeldoorn, D.F.[Dirk Frederik], Suomalainen, J.[Juha], Kooistra, L.[Lammert],
Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902

Gold, K.M.[Kaitlin M.], Townsend, P.A.[Philip A.], Chlus, A.[Adam], Herrmann, I.[Ittai], Couture, J.J.[John J.], Larson, E.R.[Eric R.], Gevens, A.J.[Amanda J.],
Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001

Clevers, J.G.P.W.[Jan G.P.W.], Kooistra, L.[Lammert], van den Brande, M.M.M.[Marnix M. M.],
Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706

Liu, N.[Ning], Xing, Z.Z.[Zi-Zheng], Zhao, R.M.[Ruo-Mei], Qiao, L.[Lang], Li, M.[Minzan], Liu, G.[Gang], Sun, H.[Hong],
Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009

Duarte-Carvajalino, J.M.[Julio M.], Alzate, D.F.[Diego F.], Ramirez, A.A.[Andrés A.], Santa-Sepulveda, J.D.[Juan D.], Fajardo-Rojas, A.E.[Alexandra E.], Soto-Suárez, M.[Mauricio],
Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Teng, P.[Poching], Ono, E.[Eiichi], Zhang, Y.[Yu], Aono, M.[Mitsuko], Shimizu, Y.[Yo], Hosoi, F.[Fumiki], Omasa, K.[Kenji],
Estimation of Ground Surface and Accuracy Assessments of Growth Parameters for a Sweet Potato Community in Ridge Cultivation,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907

Fernández, C.I.[Claudio Ignacio], Leblon, B.[Brigitte], Haddadi, A.[Ata], Wang, K.[Keri], Wang, J.F.[Jin-Fei],
Potato Late Blight Detection at the Leaf and Canopy Levels Based in the Red and Red-Edge Spectral Regions,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004

Li, B.[Bo], Xu, X.M.[Xiang-Ming], Zhang, L.[Li], Han, J.[Jiwan], Bian, C.S.[Chun-Song], Li, G.C.[Guang-Cun], Liu, J.G.[Jian-Gang], Jin, L.P.[Li-Ping],
Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging,
PandRS(162), 2020, pp. 161-172.
Elsevier DOI 2004
Unmanned aerial vehicle, Hyperspectral imaging, Potato, Above-ground biomass, Yield prediction BibRef

Afzaal, H.[Hassan], Farooque, A.A.[Aitazaz A.], Schumann, A.W.[Arnold W.], Hussain, N.[Nazar], McKenzie-Gopsill, A.[Andrew], Esau, T.[Travis], Abbas, F.[Farhat], Acharya, B.[Bishnu],
Detection of a Potato Disease (Early Blight) Using Artificial Intelligence,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102

Roosjen, P.P.J.[Peter P.J.], Suomalainen, J.M.[Juha M.], Bartholomeus, H.M.[Harm M.], Clevers, J.G.P.W.[Jan G.P.W.],
Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle: Results for Barley, Winter Wheat, and Potato,
RS(8), No. 11, 2016, pp. 909.
DOI Link 1612
Earlier: A2, A1, A3, A4:
Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer Mounted on UAV and a Laboratory Goniometer: Preliminary Results,
DOI Link 1512

Roosjen, P.P.J.[Peter P.J.], Suomalainen, J.M.[Juha M.], Bartholomeus, H.M.[Harm M.], Kooistra, L.[Lammert], Clevers, J.G.P.W.[Jan G.P.W.],
Mapping Reflectance Anisotropy of a Potato Canopy Using Aerial Images Acquired with an Unmanned Aerial Vehicle,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706

Gómez, D.[Diego], Salvador, P.[Pablo], Sanz, J.[Julia], Casanova, J.L.[Jose Luis],
Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908

Salvador, P.[Pablo], Gómez, D.[Diego], Sanz, J.[Julia], Casanova, J.L.[José Luis],
Estimation of Potato Yield Using Satellite Data at a Municipal Level: A Machine Learning Approach,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006

Appeltans, S.[Simon], Guerrero, A.[Angela], Nawar, S.[Said], Pieters, J.[Jan], Mouazen, A.M.[Abdul M.],
Practical Recommendations for Hyperspectral and Thermal Proximal Disease Sensing in Potato and Leek Fields,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006

Elsayed, S.[Salah], El-Hendawy, S.[Salah], Khadr, M.[Mosaad], Elsherbiny, O.[Osama], Al-Suhaibani, N.[Nasser], Alotaibi, M.[Majed], Tahir, M.U.[Muhammad Usman], Darwish, W.[Waleed],
Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105

Žibrat, U.[Uroš], Stare, B.G.[Barbara Geric], Knapic, M.[Matej], Susic, N.[Nik], Lapajne, J.[Janez], Širca, S.[Saša],
Detection of Root-Knot Nematode Meloidogyne luci Infestation of Potato Tubers Using Hyperspectral Remote Sensing and Real-Time PCR Molecular Methods,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105

Yang, H.B.[Hai-Bo], Li, F.[Fei], Wang, W.[Wei], Yu, K.[Kang],
Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106

Mhango, J.K.[Joseph K.], Harris, E.W.[Edwin W.], Green, R.[Richard], Monaghan, J.M.[James M.],
Mapping Potato Plant Density Variation Using Aerial Imagery and Deep Learning Techniques for Precision Agriculture,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107

Li, D.[Dan], Miao, Y.X.[Yu-Xin], Gupta, S.K.[Sanjay K.], Rosen, C.J.[Carl J.], Yuan, F.[Fei], Wang, C.Y.[Chong-Yang], Wang, L.[Li], Huang, Y.B.[Yan-Bo],
Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

Mhango, J.K.[Joseph K.], Harris, W.E.[W. Edwin], Monaghan, J.M.[James M.],
Relationships between the Spatio-Temporal Variation in Reflectance Data from the Sentinel-2 Satellite and Potato (Solanum Tuberosum L.) Yield and Stem Density,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112

Appeltans, S.[Simon], Apolo-Apolo, O.E.[Orly Enrique], Rodríguez-Vázquez, J.N.[Jaime Nolasco], Pérez-Ruiz, M.[Manuel], Pieters, J.[Jan], Mouazen, A.M.[Abdul M.],
The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112

Shi, Y.[Yue], Han, L.X.[Liang-Xiu], Kleerekoper, A.[Anthony], Chang, S.[Sheng], Hu, T.[Tongle],
Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201

Yang, H.[Huanbo], Hu, Y.[Yaohua], Zheng, Z.Z.[Zhou-Zhou], Qiao, Y.C.[Yi-Chen], Hou, B.[Bingru], Chen, J.[Jun],
A New Approach for Nitrogen Status Monitoring in Potato Plants by Combining RGB Images and SPAD Measurements,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210

Liu, Y.[Yang], Feng, H.K.[Hai-Kuan], Yue, J.[Jibo], Li, Z.H.[Zhen-Hai], Jin, X.L.[Xiu-Liang], Fan, Y.G.[Yi-Guang], Feng, Z.H.[Zhi-Hang], Yang, G.J.[Gui-Jun],
Estimation of Aboveground Biomass of Potatoes Based on Characteristic Variables Extracted from UAV Hyperspectral Imagery,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211

Liu, Y.[Yang], Feng, H.K.[Hai-Kuan], Yue, J.[Jibo], Fan, Y.G.[Yi-Guang], Jin, X.L.[Xiu-Liang], Zhao, Y.[Yu], Song, X.Y.[Xiao-Yu], Long, H.L.[Hui-Ling], Yang, G.J.[Gui-Jun],
Estimation of Potato Above-Ground Biomass Using UAV-Based Hyperspectral images and Machine-Learning Regression,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212

Liu, Y.[Yang], Feng, H.K.[Hai-Kuan], Yue, J.[Jibo], Fan, Y.G.[Yi-Guang], Jin, X.L.[Xiu-Liang], Song, X.Y.[Xiao-Yu], Yang, H.[Hao], Yang, G.J.[Gui-Jun],
Estimation of Potato Above-Ground Biomass Based on Vegetation Indices and Green-Edge Parameters Obtained from UAVs,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212

van de Vijver, R.[Ruben], Mertens, K.[Koen], Heungens, K.[Kurt], Nuyttens, D.[David], Wieme, J.[Jana], Maes, W.H.[Wouter H.], van Beek, J.[Jonathan], Somers, B.[Ben], Saeys, W.[Wouter],
Ultra-High-Resolution UAV-Based Detection of Alternaria solani Infections in Potato Fields,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212

Jindo, K.[Keiji], Teklu, M.G.[Misghina Goitom], van Boheeman, K.[Koen], Njehia, N.S.[Njane Stephen], Narabu, T.[Takashi], Kempenaar, C.[Corne], Molendijk, L.P.G.[Leendert P. G.], Schepel, E.[Egbert], Been, T.H.[Thomas H.],
Unmanned Aerial Vehicle (UAV) for Detection and Prediction of Damage Caused by Potato Cyst Nematode G. pallida on Selected Potato Cultivars,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303

Agbona, A.[Afolabi], Montesinos-Lopez, O.A.[Osval A.], Everett, M.E.[Mark E.], Ruiz-Guzman, H.[Henry], Hays, D.B.[Dirk B.],
Yield Adjustment Using GPR-Derived Spatial Covariance Structure in Cassava Field: A Preliminary Investigation,
RS(15), No. 7, 2023, pp. 1771.
DOI Link 2304

Oivukkamäki, J.[Jaakko], Atherton, J.[Jon], Xu, S.[Shan], Riikonen, A.[Anu], Zhang, C.[Chao], Hakala, T.[Teemu], Honkavaara, E.[Eija], Porcar-Castell, A.[Albert],
Investigating Foliar Macro- and Micronutrient Variation with Chlorophyll Fluorescence and Reflectance Measurements at the Leaf and Canopy Scales in Potato,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306

Ebrahimy, H.[Hamid], Wang, Y.[Yi], Zhang, Z.[Zhou],
Utilization of synthetic minority oversampling technique for improving potato yield prediction using remote sensing data and machine learning algorithms with small sample size of yield data,
PandRS(201), 2023, pp. 12-25.
Elsevier DOI 2307
Potato, Yield prediction, Machine learning, Synthetic data, SMOTE BibRef

Yu, T.[Tong], Zhou, J.[Jing], Fan, J.H.[Jia-Hao], Wang, Y.[Yi], Zhang, Z.[Zhou],
Potato Leaf Area Index Estimation Using Multi-Sensor Unmanned Aerial Vehicle (UAV) Imagery and Machine Learning,
RS(15), No. 16, 2023, pp. 4108.
DOI Link 2309

Mukiibi, A.[Alex], Franke, A.C.[Angelinus Cornelius], Steyn, J.M.[Joachim Martin],
Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI,
RS(15), No. 18, 2023, pp. 4579.
DOI Link 2310

Yin, H.[Hang], Li, F.[Fei], Yang, H.B.[Hai-Bo], Di, Y.F.[Yun-Fei], Hu, Y.C.[Yun-Cai], Yu, K.[Kang],
Mapping Plant Nitrogen Concentration and Aboveground Biomass of Potato Crops from Sentinel-2 Data Using Ensemble Learning Models,
RS(16), No. 2, 2024, pp. 349.
DOI Link 2402

Chiliquinga, M.D.[Mauricio D.], Mañay, E.D.[Edison D.], Rivera, E.F.[E. Fabián], Pilco, M.V.[Marco V.],
Virtual Training System Based on the Physiological Cycle of the Potato INIAP Suprema,
Springer DOI 2112

Piccard, I., Gobin, A., Wellens, J., Tychon, B., Goffart, J.P., Curnel, Y., Planchon, V., Leclef, A., Cools, R., Cattoor, N.,
Potato monitoring in Belgium with 'WatchITGrow',
remote sensing, Belgium, agrometeorological algorithms, back-end parameters, biophysical parameters, yield forecast BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Rapeseed Crop Analysis, Canola Analysis, Production, Detection .

Last update:May 23, 2024 at 14:31:23