Remote Sensing for phenotyping tar spot complex in maize

by Carolyn Cowan

Multispectral and thermal images taken by cameras on unmanned aerial vehicles (UAVs) are helping researchers to monitor the resistance of maize to foliar diseases.

A new study from researchers at the International Maize and Wheat Improvement Center (CIMMYT) can reduce challenges associated with plant disease assessment in the field. By deploying cameras mounted on unmanned aerial vehicles (UAVs) that capture image information from non-visible sections of the electromagnetic spectrum, the interdisciplinary team demonstrated the effectiveness of remote sensing technologies in maize disease phenotyping.    

“Plant disease resistance assessment in the field is becoming difficult because the breeders’ trials are becoming larger, the trials have to be conducted in multiple locations, and because sometimes there is a lack of highly trained personnel who can evaluate the diseases,” said Francelino Rodrigues, CIMMYT Precision Agriculture Specialist and co-lead author of the study. “In addition, the disease notes taken in the field by a human eye can vary from person to person depending on the persons’ experience.”

Preparing the UAV for radiometric calibration for multispectral flight over a maize tar spot complex screening trial. CIMMYT’s Agua Fria Experimental Station, Mexico. (Photo: Alexander Loladze/CIMMYT)

Tar spot complex (TSC) is a major foliar disease that affects maize in many regions of Latin America. It is caused by the interaction of two fungal pathogens, Phyllachora maydis and Monographella maydis, which thrive in warm, humid conditions. Named for the telltale black spots that cover infected plants, TSC causes leaves to die prematurely, which weakens the plant and hinders ear development, leading to reduced maize yields.

As remote sensing technologies become more accessible and affordable, scientists are applying it more often to such processes as phenotyping, a vital stage in the crop breeding process that involves the assessment of plants for desired agronomic or physical traits. Phenotyping is usually carried out by a team of evaluators who walk through crop plots, assessing each plant by eye – a practice that is labor- intensive and time consuming.

Recent studies report that remotely sensed data, obtained using hyperspectral, multispectral or thermal imaging, can offer accurate methods of identifying, quantifying and monitoring plant diseases and could save time and expense. Nonetheless, such technology has never been applied before to phenotyping maize foliar diseases.

The authors of the study highlight the potential of remote sensing to facilitate accurate high-throughput phenotyping for resistance to foliar diseases in maize, such as TSC, helping reduce the cost and time required for the development of improved maize germplasm.

“To phenotype maize for resistance to foliar diseases, highly trained personnel must spend hours in the field to complete visual crop evaluations, which requires substantial time and resources and may result in biased or inaccurate results between surveyors,” said Rodrigues. “The use of UAVs to gather multispectral and thermal images allows researchers to cut down the time and expenses of evaluations, and perhaps in the future it could also improve accuracy.”

Remote sensing technology

Receptors in the human eye detect a limited range of wavelengths in the electromagnetic spectrum – the area we call visible light – consisting of three bands that our eyes perceive as red, green and blue. The colors we see are the combination of the three bands of visible light that an object reflects.

Remote sensing takes advantage of how the surface of a leaf differentially absorbs, transmits and reflects light depending on its composition and condition. The reflectance of diseased plant tissue is different from that of healthy ones provided the plants are not stressed by other factors, such as heat, drought or nutrient deficiencies.

Multispectral and thermal cameras capture reflectance beyond the range of visible light and can differentiate between disease resistant and susceptible plants during phenotyping.

The study

Researchers planted 25 tropical and subtropical maize hybrids of known agronomic performance and resistance to TSC at CIMMYT’s Agua Fria Experimental Station in central Mexico. They then carried out disease assessments by eye and gathered multispectral and thermal imagery of the plots. 

Caused by the interaction of two fungal pathogens that thrive in warm, humid conditions, TSC is diagnosed by the telltale black spots that cover infected plants. (Photo: Alexander Loladze/CIMMYT)

This allowed them to compare remote sensing with traditional phenotyping methods. Calculations revealed a strong relationship between grain yield, canopy temperature, vegetation indices and the visual assessment. “The results of the study suggest that remote sensing could be used as an alternative method for assessment of disease resistance in large-scale maize trials,” said Rodrigues. “It could also be used to calculate potential losses due to tar spot complex.” 

The study found TSC could cause up to 58 percent yield loss in susceptible maize varieties in humid, lowland conditions, such as the study site.

Future applications

Accelerated breeding for agriculturally relevant crop traits is fundamental to the development of improved varieties that can face mounting global agricultural threats. It is likely that remote sensing technologies will have a critical role to play in overcoming these challenges.

“An important future area of research encompasses pre-symptomatic detection of diseases in maize,” explained Rodrigues. “If successful, such early detection would allow appropriate disease management interventions before the development of severe epidemics. Nevertheless, we still have a lot of work to do to fully integrate remote sensing into the breeding process and to transfer the technology into farmers’ fields.”

Funding for this research was provided by the CGIAR Research Program on Maize (MAIZE).

Read the full article: Loladze A, Rodrigues FA Jr, Toledo F, San Vicente F, Gérard B and Boddupalli MP (2019) Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize. Front. Plant Sci. 10:552. doi: 10.3389/fpls.2019.00552

disease, phenotyping, remote sensing, tar spot complex, UAV