Wheat (Triticum aestivum L.) serves as a fundamental staple crop, contributing approximately 20% of global caloric intake. The architecture of the wheat plant, particularly its height, plays an instrumental role in determining yield potential and structural stability. An optimal plant height balances biomass accumulation and photosynthetic capacity against risks of lodging, a phenomenon where excessively tall plants topple under environmental stresses such as wind or rain, leading to substantial yield losses. The “Green Revolution” famously harnessed dwarfing genes to reduce plant height and increase harvest index, revolutionizing global crop productivity. Yet modern breeding programs still face the challenge of precisely tuning plant height to local conditions, environments, and climate variability, necessitating novel methods to quantify this complex trait at scale.

Traditional field-based plant height assessments typically involve manual measurement of a limited number of plants within each plot, a laborious and error-prone approach that fails to fully characterize the spatial heterogeneity within plots. This issue is exacerbated by the time sensitivity and logistical difficulty of such operations, translating into delays or inaccuracies in breeding selection cycles. Recent technological advances have fostered the emergence of high-throughput phenotyping platforms, particularly UAVs outfitted with imaging sensors, enabling rapid, repeated, and non-destructive capture of crop structural traits over large experimental fields. However, classic UAV imaging strategies predominantly utilize nadir (top-down) views, which provide limited canopy perspective, particularly in densely planted or tall crops.

The present study, led by Yuntao Ma and Yonggui Xiao at China Agricultural University and the Chinese Academy of Agricultural Sciences, pioneers the use of cross-circling oblique (CCO) UAV imaging flown at ultra-low altitudes to capture wheat canopies. By flight paths circling plots from oblique angles, the system records comprehensive side and top views, yielding dense 3D point clouds that better resolve the vertical and horizontal complexity of the canopy architecture. Conducted under multi-environmental field trials, this methodology allows direct comparison against traditional nadir imaging, with both approaches flown at identical altitudes and overlap settings to ensure fair benchmarking.

Analytical reconstruction of the CCO-derived point clouds produces precise 3D canopy models from which plant height metrics can be extracted at multiple quantile levels, from lower canopy to uppermost spikes. This multi-quantile approach moves beyond simplistic average height estimations and addresses the intrinsic heterogeneity within and between plots. Of note, results demonstrate that the 90th percentile height quantile exhibits the strongest concordance with ground truth field measurements, while lower quantiles frequently underestimate height by calculating stem rather than spike height. The denser and more accurate canopy coverage afforded by CCO imaging is further validated by its superior correlation coefficients and reduced root mean square errors (RMSE) relative to nadir imaging.

Importantly, the high resolution of CCO 3D reconstructions enables visualization of detailed organ-level features, such as individual spikes within wheat plots, offering phenotyping precision unprecedented in field conditions. Although the method shows some limitations in resolving side views when planting density is exceptionally high, the overall data quality supports robust extraction of phenotypic variation critical for genetic analyses. In this study, recombinant inbred line (RIL) populations evaluated under diverse environments exhibited normal distribution patterns for both field-measured and 3D-derived plant heights, with significant correlations across quantiles and exceptional broad-sense heritability values (ranging from 0.775 to 0.982 depending on environment and quantile).

The study’s power becomes most apparent in its genetic mapping results. A comprehensive quantitative trait locus (QTL) analysis across seven environmental conditions identified 106 loci associated with plant height traits measured by both traditional and 3D methods. Among these, 40 loci were common to both approaches, but crucially, 11 loci were consistently identified only by the multi-level 3D height measurements derived from CCO imaging. The discovery of these stable, previously undetectable loci highlights the enhanced genetic resolution afforded by fine-grained phenotyping. Furthermore, two potentially novel loci, designated QPhzj.caas-3A.2 and QPhzj.caas-7A.1, have been successfully converted into Kompetitive Allele Specific PCR (KASP) molecular markers, validated across natural populations, and shown to associate with significant plant height variation under different irrigation regimes.

Candidate gene analyses anchored to these loci have pinpointed important functional genes such as Rht5, a gibberellin-sensitive dwarfing gene located on chromosome 3B, long implicated in height regulation, and TaGL3-5A on chromosome 5A, known for its influence on grain size and weight. These genetic insights are bolstered by the molecular validation via KASP markers, demonstrating the utility of integrating high-resolution phenomics with genomics for marker-assisted selection (MAS). This integration fosters accelerated breeding gains by enabling early and accurate selection for ideotype traits critical to yield and resilience.

The implications of deploying UAV CCO imaging for multi-level 3D plant height measurement extend beyond wheat. The technique’s scalability, cost-effectiveness, and precision position it as a paradigm-shifting tool for phenotyping diverse crops where canopy architecture and height are agronomically important. As such, this approach aligns seamlessly with emerging trends in digital agriculture and precision phenomics, offering researchers and breeders enhanced capacity to dissect complex traits, monitor crop responses to environmental variables, and optimize genetic improvement pipelines.

This pioneering research not only addresses long-standing technical constraints in field-based phenotyping but also establishes a versatile framework for integrating UAV remote sensing, 3D modeling, and quantitative genetics into routine breeding. As agriculture faces mounting challenges from climate change, resource limitations, and growing food demand, innovations like these are essential for unlocking new genetic potentials and tailoring crops to future environments with unprecedented speed and accuracy.

By providing a refined, multi-dimensional perspective of plant height and its genetic underpinnings, the UAV CCO imaging method represents a transformative advance empowering breeders with actionable data and enabling precision selection strategies. Ultimately, this technology promises to accelerate the development of high-yielding, lodging-resistant wheat cultivars, contributing to global food security and sustainable agricultural intensification.

Subject of Research:
Wheat plant height phenotyping and genetic mapping using UAV-based 3D canopy modeling.

Article Title:
Genetic resolution of multi-level plant height in common wheat using the 3D canopy model from ultra-low altitude unmanned aerial vehicle imagery

News Publication Date:
28 February 2025

References:
DOI: 10.1016/j.plaphe.2025.100017

Keywords:
Agriculture, Technology, Biomedical engineering

Harnessing the power of unmanned aerial vehicles (UAVs) equipped with hyperspectral and thermal cameras, the research team conducted extensive field experiments to monitor the physiological and biochemical traits of several hundred wheat genotypes grown under both well-watered and rain-out drought conditions. These drones captured highly detailed images that record variations in leaf thermal emission and light reflectance patterns, which are key indicators of critical plant traits such as stomatal conductance, leaf area index, and chlorophyll content. Such parameters directly relate to the plant’s water-use efficiency and photosynthetic capacity, offering a window into how different wheat lines manage water loss and carbon assimilation under environmental stress.

Traditionally, stomatal conductance — the rate at which CO₂ enters and water vapor exits the leaf through microscopic pores — has been measured by cumbersome, low-throughput instruments such as porometers, which require close contact with plants and considerable manual labor. This has severely limited large-scale genetic studies aimed at understanding plant physiological responses to stress. The revolutionary UAV-based approach developed by Ph.D. candidate Roy Sadeh, under the expert supervision of Dr. Ittai Herrmann and Prof. Zvi Peleg, circumvents these limitations by remotely acquiring thousands of data points rapidly across diverse germplasm collections, all without physically disturbing the plants.

Over two full growing seasons, the team’s drone flights at the Pheno-IL research facility involved capturing multiple spectral bands and thermal data, which were then fused into comprehensive phenotypic profiles through sophisticated computational models. Utilizing support vector machine algorithms—a subset of machine learning—the researchers translated raw imagery into quantitative estimates of water-use traits with an impressive 28% increase in accuracy over previous methods. This computational framework represents a significant advancement in transforming raw sensor data into biologically meaningful metrics at scale.

Crucially, integrating these precise phenotypic measurements with high-density wheat genotyping enabled a powerful genome-wide association study (GWAS). This analysis revealed 16 genetic loci significantly correlated with enhanced performance under both optimal and drought stress conditions, marking a pioneering stride in linking remotely sensed physiological traits with underlying genetic architectures. These genetic markers were subsequently validated in a follow-up field trial, cementing their potential utility as targets in wheat breeding programs.

The implications of this study extend far beyond academic curiosity. By enabling a high-throughput, non-invasive, and genetically informed phenotyping pipeline, the research team has effectively unlocked a fast track for breeders to select wheat lines that exhibit superior drought tolerance and carbon assimilation efficiency. This technology-driven breeding paradigm aligns perfectly with global efforts to build climate-resilient food systems, particularly as rising temperatures and erratic precipitation patterns increasingly jeopardize crop yields worldwide.

As Roy Sadeh aptly explains, “Our drone-based method fundamentally changes the pace and scale at which we can identify plants with desirable physiological traits. It empowers us to plant the seeds for future crop varieties that are better prepared to thrive in increasingly dry and hot environments, ultimately securing food production for generations ahead.” This statement underscores a paradigm shift in agricultural science where multidisciplinary innovations—from remote sensing and computational biology to genetics—converge to meet one of humanity’s most pressing challenges.

The research is particularly noteworthy for its utilization of hyperspectral imaging, which captures reflectance data across a broad spectrum of wavelengths invisible to the naked eye. This spectral richness enables decoding of subtle variations in leaf chemistry and structure, such as pigment concentration and canopy architecture, which are intimately tied to photosynthesis and water regulation. Coupled with thermal infrared imaging that detects leaf surface temperatures, the combined imaging modalities offer a multidimensional picture of plant health and stress responses in real time.

Furthermore, the experimental setting—a rain-out shelter at the Pheno-IL facility—allowed the team to precisely simulate drought stress conditions while maintaining uniform environmental controls. This setup ensured the reliability of trait measurements and the relevance of the findings to actual field situations where water scarcity is a prevailing concern. The rigorous validation approach, including the replication of genetic associations in independent trials, enhances confidence in the robustness and applicability of the results.

From a technological perspective, this study exemplifies how the fusion of airborne remote sensing platforms with machine learning analytics is revolutionizing precision agriculture. Support vector machines enabled pattern recognition and complex trait prediction beyond the capabilities of traditional statistical methods, highlighting the immense potential of artificial intelligence to decipher vast biological datasets. As computational power continues to grow and sensor technologies improve, such integrative approaches promise to become standard tools in crop improvement initiatives worldwide.

In summary, the integration of UAV-borne hyperspectral and thermal imaging with genome-wide genetic analyses opens a transformative new pathway in plant science and breeding. This innovative methodology offers a scalable, efficient, and precise means to dissect complex physiological processes like stomatal conductance at the genetic level, accelerating the development of wheat varieties that can endure the multifaceted stresses imposed by climate change. As global agriculture stands at a critical juncture, such forward-thinking research not only advances scientific understanding but also delivers concrete solutions critical for sustaining the food supply in a warming world.

The study, published on April 19, 2025, in the journal Computers and Electronics in Agriculture, represents a landmark achievement in crop phenomics and genetics. Supported by the Israeli Council for Higher Education’s Future Crops for Carbon Farming project, the Dutch Ministry of Foreign Affairs, and the Chief Scientist of the Israeli Ministry of Agriculture and Food Security, this work exemplifies the importance of international cooperation and innovation-driven funding in addressing global agricultural challenges.

Looking ahead, the research team envisions wider applications of UAV-based phenotyping across other crop species and stress conditions. By refining imaging and analytic technologies and expanding genetic databases, the precision breeding revolution sparked by this study promises to equip farmers and breeders with unprecedented tools to combat the uncertainties of climate change. As food security continues to dominate global priorities, this fusion of drone technology, spectroscopy, and genomics may well become one of agriculture’s most powerful weapons.

Subject of Research: Not applicable

Article Title: UAV-borne hyperspectral and thermal imagery integration empowers genetic dissection of wheat stomatal conductance

News Publication Date: 19-Apr-2025

Web References: http://dx.doi.org/10.1016/j.compag.2025.110411

Image Credits: Roy Sadeh, Ittai Herrmann, Prof. Zvi Peleg

Keywords: Agriculture, Crop domestication, Crop irrigation, Crop production, Crop science, Food science, Environmental sciences

Utilizing a drone equipped with a basic RGB camera and free software tools, the researchers have significantly streamlined the process of plant selection under drought conditions. Unlike conventional methods that often rely on costly multispectral imaging equipment, this new technique is not only more affordable but also allows for more efficient data collection, enhancing the study’s accessibility for smaller agricultural enterprises. With the ability to cover extensive fields in mere hours, this approach marks a noteworthy advancement in precision agriculture.

The results of this transformative research have been documented in a paper published in the Plant Phenome Journal, underscoring the scientific community’s recognition of the method’s potential to advance agricultural practices. The authors, who are affiliated with the renowned GCCRC at the State University of Campinas (UNICAMP), underscored that using a cost-effective RGB camera provided superior data collection capabilities in assessing the drought resistance of genetically modified corn varieties.

Helcio Duarte Pereira, one of the lead researchers, emphasized the practicality of this approach, explaining that the method substantially reduces the financial burden associated with experimenting on genetically modified plants. Traditionally, such experiments can be prohibitively expensive, limiting research to well-funded institutions and leaving smaller experimental setups underfunded and understudied.

During field trials conducted between April and September of 2023, the researchers gathered invaluable data on 21 varieties of corn, comprising three conventional types and 18 genetically modified variants. This experimentation occurred at a specialized testing site designed specifically for agricultural research in Campinas. The rigorous methodology allowed researchers to differentiate between plants subjected to varying water availability conditions, thus enabling a comprehensive understanding of drought tolerance.

Each drone flight lasted approximately 10 minutes and produced around 290 images, making it possible to analyze a wealth of data in a fraction of the time required by traditional methods. The research team carefully selected and compared results obtained via the low-cost RGB camera with those captured by a more advanced multispectral camera, which delivers a broader spectrum of data, including near-infrared wavelengths crucial for plant stress assessment.

Through meticulous analysis using free software, the team was able to correlate the color variations in the drone imagery with real-time, ground-based measurements of plant health. This cross-validation process not only confirmed the efficacy of the RGB camera but also enabled researchers to develop accurate predictive models for assessing drought stress in crops.

The implications of this research extend far beyond theoretical benefits. By providing a method that is both economically viable and effective, the researchers are poised to democratize access to agricultural data collection technologies. Tapping into drone capabilities has the potential to transform breeding programs and empower farmers in developing countries who may lack access to traditional high-tech solutions.

The innovative use of drones allows for ongoing regular assessments of crop performance during their growth cycles. Continuous monitoring is particularly crucial in understanding plant behavior under variable water availability scenarios, providing insights that can be adapted to upcoming growing seasons.

Moreover, the team’s development of predictive models based on their findings paves the way for future research endeavors. The indices evaluated throughout the study provide a foundation for designing applications aimed at automating water stress assessments across various crops, representing a significant leap forward in agricultural technology.

The limitations of conventional agricultural assessments—often labor-intensive and reliant on expensive tools—are being rapidly addressed through such advancements. The speed at which data can be collected and analyzed enables researchers to share findings with the broader farming community, fostering a collaborative approach to improving crop resilience in the face of climate vulnerabilities.

As the global agricultural sector grapples with the challenges posed by climate change, innovative strategies such as this undertake unprecedented importance. The ability to rapidly assess the drought resilience of crops not only benefits researchers but also feeds directly back into the ecosystem of agricultural production, boosting food security and contributing to sustainability efforts.

Undoubtedly, this pioneering method is set to inspire other research groups and startups who can explore various applications tailored to industry needs. Technologies already available in the market that assess plant chlorophyll levels and nitrogen content could further complement the advancements achieved here, leading to more comprehensive agricultural management practices and increased efficiency in resource use.

In summary, the research conducted by the GCCRC signifies a pivotal moment for the intersection of technology and agriculture. As digital tools become increasingly integrated into farming practices, we can expect to see continued advancements that enhance our ability to predict and mitigate the impacts of climate change, ensuring a resilient future for agriculture worldwide.

Subject of Research: Drought-tolerant corn plants utilizing drone technology and low-cost imaging
Article Title: Temporal field phenomics of transgenic maize events subjected to drought stress: Cross-validation scenarios and machine learning models
News Publication Date: 5-Jan-2025
Web References: Plant Phenome Journal
References: N/A
Image Credits: Paula Drummond de Castro/GCCRC
Keywords: Drought resistance, agricultural technology, precision agriculture, drone imaging, genetically modified crops, climate change, phenomics, crop resilience.