Posts Tagged ‘breeding tool’

Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques

Posted on , November 17, 2019

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly.

Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions.

We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions.

HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE) Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV.

Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials.

Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.

Maize Ear Digital Imaging for Yield Components Assessment

Posted on , April 8, 2019

In Sub-Saharan Africa, many food security programs aim to increase crop yields by developing and disseminating better seeds and agronomy to millions of smallholder farmers. In the case of maize, research and development organizations use key indicators like grain yield, or maize cob number and size to understand the performance of the maize plant under different environmental conditions. But these indicators are still labor-intensive and expensive to measure. CIMMYT has developed a digital imaging tool called Maize Ear Analyzer that collects maize cob and grain parameters 90% faster than traditional methods (Makanza et al, 2018). This imaging tool can be adapted to other crops.

Digital imaging tools make maize breeding much more efficient

Posted on Media&Stories, News, Research News, March 14, 2019

To accelerate annual genetic gains under various stress conditions, maize breeders 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 like kernel number and size per ear, leaf angles or ear heights.


Measuring maize attributes such as ear size, kernel number and kernel weight is becoming faster and simpler through digital imaging technologies.

Recent innovations in digital imagery and sensors, packaged in what plant scientists call high-throughput phenotyping platforms, save money and time by replacing lengthy paper-based visual observations of crop trials with real-time big data collection and management.

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. Under the Stress Tolerant Maize for Africa project, researchers like Mainassara Zaman-Allah use drone and create new digital tools, like the ear analyzer, for cheaper and faster plant selection. Drone cuts data collection costs by 25 to 75 percent compared to conventional methods.  The ear analyzer allows to collect maize ear and kernel trait data 90 percent faster. This mobile app has been used by CIMMYT and the GOAL NGO to assess the extent of fall armyworm impact on maize crops yield in eastern Zimbabwe.

Read more about how STMA makes maize breeding faster and cheaper here

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