Authors of a recent Crop Science article leveraged unmanned aerial vehicles (UAVs) to record the normalized difference vegetation index (NDVI), a measure of plant health, at the seed increase stage of the International Maize and Wheat Improvement Center’s (CIMMYT) wheat breeding program.
Read more here: https://www.sciencecodex.com/unmanned-aerial-vehicles-help-wheat-breeders-655650
In crop research fields, it is now a common sight to see drones or other high-tech sensing tools collecting high-resolution data on a wide range of traits — from simple measurement of canopy temperature to complex 3D reconstruction of photosynthetic canopies.
This technological approach to collecting precise plant trait information, known as phenotyping, is becoming ubiquitous, but according to experts at the International Maize and Wheat Improvement Center (CIMMYT) and other research institutions, breeders can profit much more from these tools, when used judiciously.
In a new article in the journal Plant Science, CIMMYT researchers outline the different ways in which phenotyping can assist breeding — from large-scale screening to detailed physiological characterization of key traits — and why this methodology is crucial for crop improvement.
“While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models,” explain the authors.
Their review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translations research to deliver other new breeding resources, and how these different categories of phenotyping apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.
The authors make the case for breeders to invest in phenotyping, particularly in light of the imperative to breed crops for warmer and harsher climates. However, wide scale adoption of sophisticated phenotyping methods will only occur if new techniques add efficiency and effectiveness.
In this sense, “breeder-friendly” phenotyping should complement existing breeding approaches by cost-effectively increasing throughput during segregant selection and adding new sources of validated complex traits to crossing blocks. With this in mind, stringent criteria need to be applied before new traits or phenotyping protocols are incorporated into mainstream breeding pipelines.
Read the full article in Plant Science:
Breeder friendly phenotyping.
See more recent publications from CIMMYT researchers:
In crop research fields, drones and other high-tech sensing tools are now a common sight. They collect high-resolution data on a wide range of traits — from simple measurement of canopy temperature to complex 3D reconstruction of photosynthetic canopies.
This technological approach to collecting precise plant trait information, known as phenotyping, is becoming ubiquitous. According to experts at the International Maize and Wheat Improvement Center (CIMMYT) and other research institutions, breeders can profit much more from these tools, when used judiciously.
In a new article in the journal Plant Science, CIMMYT Wheat Physiologist Matthew Reynolds and colleagues explain the different ways that phenotyping can assist breeding — from simple to use, “handy” approaches for large scale screening, to detailed physiological characterization of key traits to identify new parental sources — and why this methodology is crucial for crop improvement. The authors make the case for breeders to invest in phenotyping, particularly in light of the imperative to breed crops for warmer and harsher climates.
Read the full article:
Breeder friendly phenotyping.
This article was originally published on WHEAT.
Cover photo: Remote sensing specialist Francisco Pinto operates a UAV at CIMMYT’s research station in Ciudad Obregón, in Mexico’s Sonora state.
A new study from researchers at the International Maize and Wheat Improvement Center (CIMMYT) shows that remote sensing can speed up and improve the effectiveness of disease assessment in experimental maize plots, a process known as phenotyping.
The study constitutes the first time that unmanned aerial vehicles (UAVs, commonly known as drones) with cameras that capture non-visible electromagnetic radiation were used to assess tar spot complex on maize.
The interdisciplinary team found among other things that potential yield losses under heavy tar spot complex infections could reach 58% — more than 10% greater than reported in previous studies.
“Plant disease resistance assessment in the field is becoming difficult because breeders’ trials are larger, are conducted at multiple locations, and there is a lack of personnel trained to evaluate diseases,” said Francelino Rodrigues, CIMMYT precision agriculture specialist and co-lead author of the study. “In addition, disease scoring based on visual assessments can vary from person to person.”
A major foliar disease that affects maize throughout Latin America, tar spot complex results from the interaction of two species of fungus that thrive in warm, humid conditions. The disease causes telltale black spots on infected plants, killing leaves, weakening the plant, and impairing ear development.
Phenotyping has traditionally involved breeders walking through crop plots and visually assessing each plant, a labor-intensive and time-consuming process. As remote sensing technologies become more accessible and affordable, scientists are applying them more often to assess experimental plants for desired agronomic or physical traits, according to Rodrigues, who said they can facilitate accurate, high-throughput phenotyping for resistance to foliar diseases in maize and help reduce the cost and time of developing 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.”
Technology sheds new light on phenotyping
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 or other electromagnetic radiation, 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.
In this study, researchers planted 25 tropical and subtropical maize hybrids of known agronomic performance and resistance to tar spot complex at CIMMYT’s experimental station in Agua Fría, central Mexico. They then carried out disease assessments by eye and gathered multispectral and thermal imagery of the plots.
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.
Future applications
“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.”
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
To keep up with growing maize demand, breeders aim at optimizing annual yield gain under various stress conditions, like drought or low fertility soils. To that end, they identify the genetic merit of each individual plant, so they can select the best ones for breeding.
To improve that process, researchers at the International Maize and Wheat Improvement Center (CIMMYT) are looking at cost-effective ways to assess a larger number of maize plants and to collect more accurate data related to key plant characteristics. Plant phenotyping looks at the interaction between the genetic make-up of a plant with the environment, which produces certain characteristics or traits. In maize, for example, this may manifest in different leaf angles or ear heights.
Recent innovations in digital imagery and sensors save money and time in the collection of data related to phenotyping. These technologies, known as high-throughput phenotyping platforms, replace lengthy paper-based visual observations of crop trials.
Authors of a recent review study on high-throughput phenotyping tools observe that obtaining accurate and inexpensive estimates of genetic value of individuals is central to breeding. Mainassara Zaman-Allah, an abiotic stress phenotyping specialist at CIMMYT in Zimbabwe and one of the co-authors, emphasizes the importance of improving existing tools and developing new ones. “Plant breeding is a continuously evolving field where new tools and methods are used to develop new varieties more precisely and rapidly, sometimes at reduced financial resources than before,” he said. “All this happens to improve efficiency in breeding, in order to address the need for faster genetic gain and reduction of the cost of breeding.”
“Under the Stress Tolerant Maize for Africa (STMA) project, we are working on implementing the use of drone-based sensing, among other breeding innovations, to reduce time and cost of phenotyping, so that the development of new varieties costs less,’’ said Zaman-Allah. “The use of drones cuts time and cost of data collection by 25 to 75 percent compared to conventional methods, because it enables to collect data on several traits simultaneously — for example canopy senescence and plant count,” he explained.
Another great innovation developed under this CIMMYT project is what Zaman-Allah calls the ear analyzer. This low-cost digital imaging app allows to collect maize ear and kernel trait data 90 percent faster. This implies higher productivity and rigor, as more time is dedicated to data analysis rather than time spent on data collection. Using digital image processing, the ear analyzer gives simultaneous data of more than eight traits, including ear size and number, kernel number, size and weight.
Some national agricultural research systems and NGOs have adopted this digital imagery tool to better assess maize yields in farmers’ fields. For instance, CIMMYT and GOAL have used this tool to assess the extent of fall armyworm impact on maize crops yield in eastern Zimbabwe.
Scientists are exploring the use of different sensors for phenotyping, such as Red, Green and Blue (RGB) digital imaging or Light Detection and Ranging (LIDAR) devices. Infrared thermal and spectral cameras could lead to further progress towards faster maize breeding.
Such sensors can help collect numerous proxy data relating to important plant physiological traits or the plant environment, like plant height and architecture, soil moisture and root characteristics. This data can be used to assess the maize crop yield potential and stress tolerance.
Such breeding innovations are also making maize research more responsive to climate change and emerging pests and diseases.
Think of all the things you do with your cell phone on any given day. You can start your car, buy a coffee and even measure your heart rate. Cell phones are our alarm clocks and our cameras, our gyms and our banks. Cell phones are not just relevant for urban living but offer an opportunity to transform the lives of smallholders beyond compare. Even the most basic handset can empower farmers by providing them with instant information on weather, crop prices, and farming techniques.
For many farmers in the developing world, cell phones are the most accessible form of technology, but are only one of many technologies changing agriculture. Innovations such as the plow, irrigation and fertilizer have shaped the history of humankind. Today, technologies continue to play an essential role in agricultural production and impact the life of farmers everywhere.
Enter the era of hyper precision
Precision farming has been around for more than 30 years, but cheaper and more robust technologies are ushering in an era of hyper precision. With increasing climate uncertainties and price fluctuations, farmers can’t afford risk, and precision agriculture enables them to increase production and profits by linking biophysical determinants and variations in crop yield. A variety of farm equipment is being equipped with GPS and sensors that can measure water needs in the crop and nutrient levels in the soil, and dispense exactly the right amount of fertilizer and water as needed.
Precision agriculture may originate from large-scale, well-resourced farms, but its concept is highly transferable and it is scale independent. The pocket-sized active-crop canopy sensors, is already a game changing technology with the potential to bring precision agriculture within the reach of smallholders. Using such sensors to read crop health provides farmers with basic information that can be used for recommended nitrogen application. This has a dual purpose, both for smallholder farmers in areas where soils typically lack nitrogen, and those that over-fertilize while simultaneously reducing profitability and causing environmental pollution.
In Bangladesh, CIMMYT researchers are developing an irrigation scheduling app that predicts a week ahead of time whether a particular field requires irrigation. Based on satellite-derived estimates of crop water use, a soil water model and weather forecasts, the underlying algorithm for the app is also being tested in the north of Mexico.
The eyes in the sky
The human eye is a remote sensor, but on a farm there are many things that cannot be seen with the unaided eye, including surface temperatures and crop changes caused by extreme weather. At CIMMYT, remote sensing devices are allowing researchers to obtain information about a large area without physical contact that would otherwise be difficult to monitor. Indeed, last month I joined researchers at CIMMYT Headquarters in El Batan, Mexico, to learn more about the use of an Unmanned Aerial Vehicle (UAV) with built-in GPS and thermal and multispectral sensors that captures aerial photography to an image resolution of 3 cm. This device is being used to capture the canopy temperature and nitrogen status of crops.
Remote sensing alone is not going to teach a farmer how to properly sow a field, take the best care of his crops or optimize returns. Remote sensing explores spatial and temporal dimensions to provide a diagnosis but the next crucial step is to turn this into recommendations on nutrient management, irrigation and crop protection. The next question is how to bring these recommendations to small farms. In a low-tech setting, this depends on knowledge transfer to provide recommendations to farmers.
