High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging

In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection.

[1]  L. Borrás,et al.  Leaf senescence in maize hybrids: plant population, row spacing and kernel set effects , 2003 .

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

[3]  Jan G. P. W. Clevers,et al.  A simplified approach for yield prediction of sugar beet based on optical remote sensing data , 1997 .

[4]  N. Ramankutty,et al.  Recent patterns of crop yield growth and stagnation , 2012, Nature Communications.

[5]  Stephan J. Maas,et al.  Mapping crop ground cover using airborne multispectral digital imagery , 2009, Precision Agriculture.

[6]  John R. Miller,et al.  Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy , 2005 .

[7]  M. Zaman-Allah,et al.  Gains in Maize Genetic Improvement in Eastern and Southern Africa: I. CIMMYT Hybrid Breeding Pipeline , 2017 .

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

[9]  Pablo J. Zarco-Tejada,et al.  Using hyperspectral remote sensing to map grape quality in 'Tempranillo' vineyards affected by iron deficiency chlorosis , 2007 .

[10]  M. A. Bacarin,et al.  Physiological analysis of leaf senescence of two rice cultivars with different yield potential , 2009 .

[11]  Malia A. Gehan,et al.  A Versatile Phenotyping System and Analytics Platform Reveals Diverse Temporal Responses to Water Availability in Setaria. , 2015, Molecular plant.

[12]  Gary E. Varvel,et al.  Light Reflectance Compared with Other Nitrogen Stress Measurements in Corn Leaves , 1994 .

[13]  P. Zarco-Tejada,et al.  Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize , 2015, Plant Methods.

[14]  William D. Philpot,et al.  Toward the Discrimination of Manganese, Zinc, Copper, and Iron Deficiency in ‘Bragg’ Soybean Using Spectral Detection Methods , 2000 .

[15]  R. Amasino,et al.  Making Sense of Senescence (Molecular Genetic Regulation and Manipulation of Leaf Senescence) , 1997, Plant physiology.

[16]  Josep Peñuelas,et al.  A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers , 2016, Proceedings of the National Academy of Sciences.

[17]  H. Nam,et al.  Leaf senescence. , 2007, Annual review of plant biology.

[18]  Qin Zhang,et al.  A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.

[19]  M. Zaman-Allah,et al.  Gains in Maize Genetic Improvement in Eastern and Southern Africa: II. CIMMYT Open-Pollinated Variety Breeding Pipeline , 2017 .

[20]  K. Chenu,et al.  PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. , 2006, The New phytologist.

[21]  Graeme L. Hammer,et al.  Multi-Spectral Imaging from an Unmanned Aerial Vehicle Enables the Assessment of Seasonal Leaf Area Dynamics of Sorghum Breeding Lines , 2017, Front. Plant Sci..

[22]  Charles F. Fifield,et al.  Image Analysis Compared with Other Methods for Measuring Ground Cover , 2005 .

[23]  Jose A. Jiménez-Berni,et al.  Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping , 2014 .

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

[25]  L. Borrás,et al.  Maize Kernel Composition and Post-Flowering Source-Sink Ratio , 2002 .

[26]  William D. Philpot,et al.  Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation , 1999 .

[27]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[28]  P. Zarco-Tejada,et al.  A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization , 2016, Front. Plant Sci..

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

[30]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[31]  James S. Schepers,et al.  Aerial Photography to Detect Nitrogen Stress in Corn , 1996 .

[32]  R. Arteca.  Juvenility, Maturity and Senescence , 1996 .