Potato Crop Analysis, Production, Detection, Health, Change

Chapter Contents (Back)
Classification. Potato.

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.[Zizheng], Zhao, R.[Ruomei], 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.[Jinfei],
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.[Xiangming], Zhang, L.[Li], Han, J.[Jiwan], Bian, C.[Chunsong], Li, G.[Guangcun], Liu, J.[Jiangang], 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

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
Pasture, Grassland, Rangeland Analysis and Change .

Last update:Jun 9, 2021 at 21:04:26