Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology

[1]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[2]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[3]  R. Crippen Calculating the vegetation index faster , 1990 .

[4]  F. Baret,et al.  About the soil line concept in remote sensing , 1993 .

[5]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[6]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[7]  N. Goel,et al.  Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation , 1994 .

[8]  Josep Peñuelas,et al.  Evaluating Wheat Nitrogen Status with Canopy Reflectance Indices and Discriminant Analysis , 1995 .

[9]  Y. H. Kerr,et al.  Critical assessment of vegetation indices from AVHRR in a semi-arid environment , 1996 .

[10]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[11]  J. Chen Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[12]  A. Gitelson,et al.  Remote sensing of chlorophyll concentration in higher plant leaves , 1998 .

[13]  K. Hibbard,et al.  A Global Terrestrial Monitoring Network Integrating Tower Fluxes, Flask Sampling, Ecosystem Modeling and EOS Satellite Data , 1999 .

[14]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[15]  G. Carter,et al.  Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. , 2001, American journal of botany.

[16]  N. Broge,et al.  Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture , 2002 .

[17]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[18]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[19]  A. Gitelson,et al.  Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction , 2002 .

[20]  A. Viña,et al.  Remote estimation of leaf area index and green leaf biomass in maize canopies , 2003 .

[21]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[22]  Dana E. Veron,et al.  First measurements of the Twomey indirect effect using ground‐based remote sensors , 2003 .

[23]  B. Pinty,et al.  GEMI: a non-linear index to monitor global vegetation from satellites , 1992, Vegetatio.

[24]  A. Viña,et al.  Remote estimation of canopy chlorophyll content in crops , 2005 .

[25]  J. G. White,et al.  Aerial Color Infrared Photography for Determining Early In‐Season Nitrogen Requirements in Corn , 2005 .

[26]  Pablo J. Zarco-Tejada,et al.  Temporal and Spatial Relationships between within-field Yield variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery , 2005 .

[27]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[28]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

[29]  R. Mittler,et al.  Genetic engineering for modern agriculture: challenges and perspectives. , 2010, Annual review of plant biology.

[30]  Takeshi Motohka,et al.  Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology , 2010, Remote. Sens..

[31]  A. Viña,et al.  Comparison of different vegetation indices for the remote assessment of green leaf area index of crops , 2011 .

[32]  Jiang Miao Eco-environmental Variables Estimation from Remotely Sensed Data and Eco-environmental Assessment:Models and System , 2011 .

[33]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[34]  J. Baluja,et al.  Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV) , 2012, Irrigation Science.

[35]  V. Salokhe,et al.  Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation , 2012, Precision Agriculture.

[36]  A. Baghestani,et al.  How to control confounding effects by statistical analysis , 2012, Gastroenterology and hepatology from bed to bench.

[37]  R. Tuberosa Phenotyping for drought tolerance of crops in the genomics era , 2012, Front. Physio..

[38]  J. Foley,et al.  Yield Trends Are Insufficient to Double Global Crop Production by 2050 , 2013, PloS one.

[39]  Laurent Tits,et al.  Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery , 2013, Remote. Sens..

[40]  Ranga B. Myneni,et al.  Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies , 2013 .

[41]  R. Bro,et al.  Diagnosing latent copper deficiency in intact barley leaves (Hordeum vulgare, L.) using near infrared spectroscopy. , 2013, Journal of agricultural and food chemistry.

[42]  P. Zarco-Tejada,et al.  Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle , 2014, Precision Agriculture.

[43]  F. López-Granados,et al.  Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images , 2013, PloS one.

[44]  Bruno Basso,et al.  Assessing the Robustness of Vegetation Indices to Estimate Wheat N in Mediterranean Environments , 2014, Remote. Sens..

[45]  Olivier Roupsard,et al.  Leaf area index as an indicator of ecosystem services and management practices: An application for coffee agroforestry , 2014 .

[46]  J. Araus,et al.  Field high-throughput phenotyping: the new crop breeding frontier. , 2014, Trends in plant science.

[47]  Tetsuji Ota,et al.  Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest , 2015 .

[48]  Lav R. Khot,et al.  Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand , 2015, Comput. Electron. Agric..

[49]  S. Sankaran,et al.  Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review , 2015 .

[50]  Yafit Cohen,et al.  Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars , 2015, Comput. Electron. Agric..

[51]  Kenji Omasa,et al.  Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces , 2015, Journal of experimental botany.

[52]  J. Bailey-Serres,et al.  Genetic mechanisms of abiotic stress tolerance that translate to crop yield stability , 2015, Nature Reviews Genetics.

[53]  Eija Honkavaara,et al.  Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level , 2015, Remote. Sens..

[54]  F. A. Vega,et al.  Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop , 2015 .

[55]  Juha Suomalainen,et al.  Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[56]  Jennifer J. Trapp,et al.  Quantitative Trait Loci for Yield under Multiple Stress and Drought Conditions in a Dry Bean Population , 2015 .

[57]  Z. Nagy,et al.  Connection between normalized difference vegetation index and yield in maize , 2016 .

[58]  Lav R. Khot,et al.  Evaluation of ground, proximal and aerial remote sensing technologies for crop stress monitoring , 2016 .

[59]  Lav R. Khot,et al.  Low altitude remote sensing technologies for crop stress monitoring: a case study on spatial and temporal monitoring of irrigated pinto bean , 2018, Precision Agriculture.

[60]  Patrick S Schnable,et al.  A high-throughput , field-based phenotyping technology for tall biomass crops , 2018 .

[61]  Hao Yang,et al.  Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..

[62]  Chandra A. Madramootoo,et al.  Recent advances in crop water stress detection , 2017, Comput. Electron. Agric..

[63]  Baofeng Su,et al.  Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications , 2017, J. Sensors.

[64]  Virendra Tewari,et al.  On-The-Go Position Sensing and Controller Predicated Contact-Type Weed Eradicator , 2018 .

[65]  L. Khot,et al.  The impact of tillage on pinto bean cultivar response to drought induced by deficit irrigation , 2018, Soil and Tillage Research.

[66]  Virendra Tewari,et al.  Sonar Sensing Predicated Automatic Spraying Technology for Orchards , 2018, Current Science.

[67]  Olivier de Weck,et al.  Satellite constellation design algorithm for remote sensing of diurnal cycles phenomena , 2018 .

[68]  Lav R. Khot,et al.  Thermal-RGB imager derived in-field apple surface temperature estimates for sunburn management , 2018 .

[69]  C. Chethan,et al.  A mechatronically integrated autonomous seed material generation system for sugarcane: A crop of industrial significance , 2019, Industrial Crops and Products.