Phenotyping in the fields: dissecting the genetics of quantitative traits and digital farming

AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.

Automated stomata detection in oil palm with convolutional neural network

Stomatal density is an important trait for breeding selection of drought tolerant oil palms; however, its measurement is extremely tedious. To accelerate this process, we developed an automated system. Leaf samples from 128 palms ranging from nursery (1 years old), juvenile (2–3 years old) and mature (> 10 years old) were collected to build an oil palm specific stomata detection model. Micrographs were split into tiles, then used to train a stomata object detection convolutional neural network model through transfer learning. The detection model was then tested on leaf samples acquired from three independent oil palm populations of young seedlings (A), juveniles (B) and productive adults (C). The detection accuracy, measured in precision and recall, was 98.00% and 99.50% for set A, 99.70% and 97.65% for set B, and 99.55% and 99.62% for set C, respectively. The detection model was cross-applied to another set of adult palms using stomata images taken with a different microscope and under different conditions (D), resulting in precision and recall accuracy of 99.72% and 96.88%, respectively. This indicates that the model built generalized well, in addition has high transferability. With the completion of this detection model, stomatal density measurement can be accelerated. This in turn will accelerate the breeding selection for drought tolerance.

Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production

Aerial imagery is regularly used by crop researchers, growers and farmers to monitor crops during the growing season. To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic analysis solutions are required, which not only produce high-quality measures of key crop traits, but also support professionals to make prompt and reliable crop management decisions. Here, we report AirSurf, an automated and open-source analytic platform that combines modern computer vision, up-to-date machine learning, and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial imagery. To quantify millions of in-field lettuces acquired by fixed-wing light aircrafts equipped with normalised difference vegetation index (NDVI) sensors, we customised AirSurf by combining computer vision algorithms and a deep-learning classifier trained with over 100,000 labelled lettuce signals. The tailored platform, AirSurf-Lettuce, is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution across the field, based on which associated global positioning system (GPS) tagged harvest regions have been identified to enable growers and farmers to conduct precision agricultural practises in order to improve the actual yield as well as crop marketability before the harvest.

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

A transnational and holistic breeding approach is needed for sustainable wheat production in the Baltic Sea region

The Baltic Sea is one of the largest brackish water bodies in the world. Eutrophication is a major concern in the Baltic Sea due to the leakage of nutrients to the sea with agriculture being the primary source. Wheat (Triticum aestivum L.) is the most widely grown crop in the countries surrounding the Baltic Sea and thus promoting sustainable agriculture practices for wheat cultivation will have a major impact on reducing pollution in the Baltic Sea. This approach requires identifying and addressing key challenges for sustainable wheat production in the region. Implementing new technologies for climate-friendly breeding and digital farming across all surrounding countries should promote sustainable intensification of agriculture in the region. In this review, we highlight major challenges for wheat cultivation in the Baltic Sea region and discuss various solutions integrating transnational collaboration for pre-breeding and technology sharing to accelerate development of low input wheat cultivars with improved host plant resistance to pathogen and enhanced adaptability to the changing climate.

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

Integration of multi-omics techniques and physiological phenotyping within a holistic phenomics approach to study senescence in model and crop plants

The study of senescence in plants is complicated by diverse levels of temporal and spatial dynamics as well as the impact of external biotic and abiotic factors and crop plant management. Whereas the molecular mechanisms involved in developmentally regulated leaf senescence are very well understood, in particular in the annual model plant species Arabidopsis, senescence of other organs such as the flower, fruit, and root is much less studied as well as senescence in perennials such as trees. This review addresses the need for the integration of multi-omics techniques and physiological phenotyping into holistic phenomics approaches to dissect the complex phenomenon of senescence. That became feasible through major advances in the establishment of various, complementary 'omics' technologies. Such an interdisciplinary approach will also need to consider knowledge from the animal field, in particular in relation to novel regulators such as small, non-coding RNAs, epigenetic control and telomere length. Such a characterization of phenotypes via the acquisition of high-dimensional datasets within a systems biology approach will allow us to systematically characterize the various programmes governing senescence beyond leaf senescence in Arabidopsis and to elucidate the underlying molecular processes. Such a multi-omics approach is expected to also spur the application of results from model plants to agriculture and their verification for sustainable and environmentally friendly improvement of crop plant stress resilience and productivity and contribute to improvements based on postharvest physiology for the food industry and the benefit of its customers.

Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery

Non-destructive monitoring of crop development is of key interest for agronomy and crop breeding. Crop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this. In this study, fresh and dry barley biomass per plot are estimated from CSM-derived plot-wise plant heights. The CSMs are generated in a semi-automated manner using Structure-from-Motion (SfM)/Multi-View-Stereo (MVS) software from oblique stereo RGB images. The images were acquired automatedly from consumer grade smart cameras mounted at an elevated position on a lifting hoist. Fresh and dry biomass were measured destructively at four dates each in 2014 and 2015. We used exponential and simple linear regression based on different calibration/validation splits. Coefficients of determination R 2 between 0.55 and 0.79 and root mean square errors (RMSE) between 97 and 234 g/m2 are reached for the validation of predicted vs. observed dry biomass, while Willmott’s refined index of model performance d r ranges between 0.59 and 0.77. For fresh biomass, R 2 values between 0.34 and 0.61 are reached, with root mean square errors (RMSEs) between 312 and 785 g/m2 and d r between 0.39 and 0.66. We therefore established the possibility of using this novel low-cost system to estimate barley dry biomass over time.

Phenotyping field-state wheat root system architecture for root foraging traits in response to environment×management interactions

An important aspect of below-ground crop physiology is its root foraging performance, which is inherently related to root system architecture (RSA). A 2-yr field experiment was conducted and the field-state wheat RSA was phenotyped for root foraging trait (RFT). Four RSA-derived traits, i.e. Root horizontal angle (RHA), axial root expansion volume (AREV), RSA convex hull volume (CHV) and effective volume per unit root length (EVURL), were analyzed for RFTs in response to environment × management interactions. Results showed a dynamical RHA process but without statistical difference both within crop seasons and tillage treatments. AREV increased with root developmental stages, revealing an overall better root performance in the first year. However, tillage treatments did not induce observed difference within both crop seasons. CHV varied drastically from year to year and between tillage treatments, correlating well to the root length, but not with RHA. EVURL was both sensitive to tillage treatments and crop seasons, being a potential indicator for RFT. Above all, tillage effect on RFT was statistically far less than that induced by crop seasons. Pro/E assisted modeling can be used as an effective means for phenotyping integrated, RSA-derived, RFTs for root foraging response to induced environment × management interactions.

In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR

Plant breeding programs and a wide range of plant science applications would greatly benefit from the development of in-field high throughput phenotyping technologies. In this study, a terrestrial LiDAR-based high throughput phenotyping system was developed. A 2D LiDAR was applied to scan plants from overhead in the field, and an RTK-GPS was used to provide spatial coordinates. Precise 3D models of scanned plants were reconstructed based on the LiDAR and RTK-GPS data. The ground plane of the 3D model was separated by RANSAC algorithm and a Euclidean clustering algorithm was applied to remove noise generated by weeds. After that, clean 3D surface models of cotton plants were obtained, from which three plot-level morphologic traits including canopy height, projected canopy area, and plant volume were derived. Canopy height ranging from 85th percentile to the maximum height were computed based on the histogram of the z coordinate for all measured points; projected canopy area was derived by projecting all points on a ground plane; and a Trapezoidal rule based algorithm was proposed to estimate plant volume. Results of validation experiments showed good agreement between LiDAR measurements and manual measurements for maximum canopy height, projected canopy area, and plant volume, with R2-values of 0.97, 0.97, and 0.98, respectively. The developed system was used to scan the whole field repeatedly over the period from 43 to 109 days after planting. Growth trends and growth rate curves for all three derived morphologic traits were established over the monitoring period for each cultivar. Overall, four different cultivars showed similar growth trends and growth rate patterns. Each cultivar continued to grow until ~88 days after planting, and from then on varied little. However, the actual values were cultivar specific. Correlation analysis between morphologic traits and final yield was conducted over the monitoring period. When considering each cultivar individually, the three traits showed the best correlations with final yield during the period between around 67 and 109 days after planting, with maximum R2-values of up to 0.84, 0.88, and 0.85, respectively. The developed system demonstrated relatively high throughput data collection and analysis.

Morphological phenotyping and genetic analyses of a new chemical-mutagenized population of tobacco (Nicotiana tabacum L.)

MAIN CONCLUSION

A novel tobacco mutant library was constructed, screened, and characterized as a crucial genetic resource for functional genomics and applied research. A comprehensive mutant library is a fundamental resource for investigating gene functions, especially after the completion of genome sequencing. A new tobacco mutant population induced by ethyl methane sulfonate mutagenesis was developed for functional genomics applications. We isolated 1607 mutant lines and 8610 mutant plants with altered morphological phenotypes from 5513 independent M2 families that consisted of 69,531 M2 plants. The 2196 mutations of abnormal phenotypes in the M2 putative mutants were classified into four groups with 17 major categories and 51 subcategories. More than 60% of the abnormal phenotypes observed fell within the five major categories including plant height, leaf shape, leaf surface, leaf color, and flowering time. The 465 M2 mutants exhibited multiple phenotypes, and 1054 of the 2196 mutations were pleiotropic. Verification of the phenotypes in advanced generations indicated that 70.63% of the M3 lines, 84.87% of the M4 lines, and 95.75% of the M5 lines could transmit original mutant phenotypes of the corresponding M2, M3, and M4 mutant plants. Along with the increased generation of mutants, the ratios of lines inheriting OMPs increased and lines with emerging novel mutant phenotypes decreased. Genetic analyses of 18 stably heritable mutants showed that two mutants were double recessive, five were monogenic recessive, eight presented monogenic dominant inheritance, and three presented semi-dominant inheritance. The pleiotropy pattern, saturability evaluation, research prospects of genome, and phenome of the mutant populations were also discussed. Simultaneously, this novel mutant library provided a fundamental resource for investigating gene functions in tobacco.

Rooting for cassava: insights into photosynthesis and associated physiology as a route to improve yield potential

Contents 50 I. 50 II. 52 III. 54 IV. 55 V. 57 VI. 57 VII. 59 60 References 61 SUMMARY: As a consequence of an increase in world population, food demand is expected to grow by up to 110% in the next 30-35 yr. The population of sub-Saharan Africa is projected to increase by > 120%. In this region, cassava (Manihot esculenta) is the second most important source of calories and contributes c. 30% of the daily calorie requirements per person. Despite its importance, the average yield of cassava in Africa has not increased significantly since 1961. An evaluation of modern cultivars of cassava showed that the interception efficiency (ɛ_i ) of photosynthetically active radiation (PAR) and the efficiency of conversion of that intercepted PAR (ɛ_c ) are major opportunities for genetic improvement of the yield potential. This review examines what is known of the physiological processes underlying productivity in cassava and seeks to provide some strategies and directions toward yield improvement through genetic alterations to physiology to increase ɛ_i and ɛ_c . Possible physiological limitations, as well as environmental constraints, are discussed.

Chlorophyll a fluorescence, under half of the adaptive growth-irradiance, for high-throughput sensing of leaf-water deficit in Arabidopsis thaliana accessions

BACKGROUND

Non-invasive and high-throughput monitoring of drought in plants from its initiation to visible symptoms is essential to quest drought tolerant varieties. Among the existing methods, chlorophyll a fluorescence (ChlF) imaging has the potential to probe systematic changes in photosynthetic reactions; however, prerequisite of dark-adaptation limits its use for high-throughput screening.

RESULTS

To improve the throughput monitoring of plants, we have exploited their light-adaptive strategy, and investigated possibilities of measuring ChlF transients under low ambient irradiance. We found that the ChlF transients and associated parameters of two contrasting Arabidopsis thaliana accessions, Rsch and Co, give almost similar information, when measured either after ~20 min dark-adaptation or in the presence of half of the adaptive growth-irradiance. The fluorescence parameters, effective quantum yield of PSII photochemistry (ΦPSII) and fluorescence decrease ratio (RFD) resulting from this approach enabled us to differentiate accessions that is often not possible by well-established dark-adapted fluorescence parameter maximum quantum efficiency of PSII photochemistry (FV/FM). Further, we screened ChlF transients in rosettes of well-watered and drought-stressed six A. thaliana accessions, under half of the adaptive growth-irradiance, without any prior dark-adaptation. Relative water content (RWC) in leaves was also assayed and compared to the ChlF parameters. As expected, the RWC was significantly different in drought-stressed from that in well-watered plants in all the six investigated accessions on day-10 of induced drought; the maximum reduction in the RWC was obtained for Rsch (16%), whereas the minimum reduction was for Co (~7%). Drought induced changes were reflected in several features of ChlF transients; combinatorial images obtained from pattern recognition algorithms, trained on pixels of image sequence, improved the contrast among drought-stressed accessions, and the derived images were well-correlated with their RWC.

CONCLUSIONS

We demonstrate here that ChlF transients and associated parameters measured even in the presence of low ambient irradiance preserved its features comparable to that of measured after dark-adaptation and discriminated the accessions having differential geographical origin; further, in combination with combinatorial image analysis tools, these data may be readily employed for early sensing and mapping effects of drought on plant's physiology via easy and fully non-invasive means.

Tragedies and Crops: Understanding Natural Selection To Improve Cropping Systems

Plant communities with traits that would maximize community performance can be invaded by plants that invest extra in acquiring resources at the expense of others, lowering the overall community performance, a so-called tragedy of the commons (TOC). By contrast, maximum community performance is usually the objective in agriculture. We first give an overview of the occurrence of TOCs in plants, and explore the extent to which past crop breeding has led to trait values that go against an unwanted TOC. We then show how linking evolutionary game theory (EGT) with mechanistic knowledge of the physiological processes that drive trait expression and the ecological aspects of biotic interactions in agro-ecosystems might contribute to increasing crop yields and resource-use efficiency.

Progress of modern agricultural chemistry and future prospects

Agriculture is facing an enormous challenge: it must ensure that enough high-quality food is available to meet the needs of a continually growing population. Current and future agronomic production of food, feed, fuel and fibre requires innovative solutions for existing and future challenges, such as climate change, resistance to pests, increased regulatory demands, renewable raw materials or requirements resulting from food chain partnerships. Modern agricultural chemistry has to support farmers to manage these tasks. Today, the so-called 'side effects' of agrochemicals regarding yield and quality are gaining more importance. Agrochemical companies with a strong research and development focus will have the opportunity to shape the future of agriculture by delivering innovative integrated solutions. This review gives a comprehensive overview of the innovative products launched over the past 10 years and describes the progress of modern agricultural chemistry and its future prospects.