A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

A high-throughput, field-based phenotyping technology for tall biomass crops

Recent advances in omics technologies have not been accompanied by equally efficient, cost-effective and accurate phenotyping methods required to dissect the genetic architecture of complex traits. Even though high-throughput phenotyping platforms have been developed for controlled environments, field-based aerial and ground technologies have only been designed and deployed for short stature crops. Therefore, we developed and tested Phenobot 1.0, an auto-steered and self-propelled field-based high-throughput phenotyping platform for tall dense canopy crops, such as sorghum (Sorghum bicolor L. Moench). Phenobot 1.0 was equipped with laterally positioned and vertically stacked stereo RGB cameras. Images collected from 307 diverse sorghum lines were reconstructed in 3D for feature extraction. User interfaces were developed and multiple algorithms were evaluated for their accuracy in estimating plant height and stem diameter. Tested feature extraction methods included: i) User-interactive Individual Plant Height Extraction based on dense stereo 3D reconstruction (UsIn-PHe); ii) Automatic Hedge-based Plant Height Extraction (Auto-PHe) based on dense stereo 3D reconstruction; iii) User-interactive Dense Stereo Matching Stem Diameter Extraction (DenS-Di); and iv) User-interactive Image Patch Stereo Matching Stem Diameter Extraction (IPaS-Di). Comparative genome-wide association analysis and ground-truth validation demonstrated that both UsIn-PHe and Auto-PHe were accurate methods to estimate plant height while Auto-PHe had the additional advantage of being a completely automated process. For stem diameter, IPaS-Di generated the most accurate estimates of this biomass-related architectural trait. In summary, our technology was proven robust to obtain ground-based high-throughput plant architecture parameters of sorghum, a tall and densely planted crop species

High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

Design and Analysis of Booms for Wheeled Mobile Platform for Crop Phenotyping

Crop phenotyping is frequently used by breeders and crop scientists to monitor the growth of plants and to relate them to genotypes of plants. Seemingly, this contributes to better crop growth and results in higher yield in solving food insecurity from growing world population. Instead of traditional crop monitoring, which is labor intensive, high-throughput phenotyping (HTP) using ground-based vehicle has several advantages over manual methods. Equipped with advanced sensors, the high-throughput phenotyping platforms quickly, accurately, and automatically, measure and record plant traits, such as appearance, height, and temperature. Although there have been many studies on plant phenotyping, there is still needs for ground-based HTP platform to perform accurate phenotyping on targeted crops (e.g. canola and wheat). Previous studies using ground-based HTP platforms focus primarily on leafy plants rather than densely cultivated crops. Besides, the previous platforms are designed for specific vehicles or sensors, and they are inappropriate for canola or wheat, which are targeted crops of this study. In this research, the main objective is to develop appropriate mechanical structures that are attached to different wheeled mobile platforms for HTP study. Using sensors attached to these mechanical booms, data are collected automatically for several traits such as height, temperature, greenness, and photos. These collected data are compared with manual measured data to evaluate the performance of the system, including suitability of mechanical structure. Three generations of the HTP platform are developed. The 1st and 2nd generation booms with simple structures use C-channel as the key component. While developing these booms, the stress, deformation, and vibration, are assessed with the finite element analysis (FEA). Meanwhile, it is necessary to understand the actual vibration pattern of these relatively long cantilever beams when attached to moving vehicles; however, previous research have little or limited investigation on vibrations influence on long booms in a farm setting. Thus, part of this research investigates how different factors, such as vehicle selection, vehicle speed, sensor locations, and road conditions, influence the boom attached to a farm machine, its vibration, and its effects on sensors performance for phenotyping. Then, an ideal operating conditions for HTP were obtained. The measurements from sensors confirm that the proposed mechanical structures and their ideal operating conditions are fulfilling the requirements for accurate sensor measurements. Finally, the 3rd generation boom/robotic arm featured of a hybrid structure is proposed and analyzed for its kinematics and dynamics suitability. Through the calculation and simulation, it shows that this robotic arm meets the requirements, including long-reach and high-payload capability, while maintaining a lightweight and relatively compact size after folding. Moreover, comparing results from path planning routines between Newton-Euler iterative method and simulations, it illustrates that they correlate well

Opportunities and limitations of crop phenotyping in southern european countries

ReviewThe Mediterranean climate is characterized by hot dry summers and frequent droughts. Mediterranean crops are frequently subjected to high evapotranspiration demands, soil water deficits, high temperatures, and photo-oxidative stress. These conditions will become more severe due to global warming which poses major challenges to the sustainability of the agricultural sector in Mediterranean countries. Selection of crop varieties adapted to future climatic conditions and more tolerant to extreme climatic events is urgently required. Plant phenotyping is a crucial approach to address these challenges. High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved genotypes and is one of the most effective strategies to improve the sustainability of agricultural production. In spite of the remarkable progress in basic knowledge and technology of plant phenotyping, there are still several practical, financial, and political constraints to implement HTPP approaches in field and controlled conditions across the Mediterranean. The European panorama of phenotyping is heterogeneous and integration of phenotyping data across different scales and translation of “phytotron research” to the field, and from model species to crops, remain major challenges. Moreover, solutions specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses) are in high demand, as the region is vulnerable to climate change and to desertification processes. The specific phenotyping requirements of Mediterranean crops have not yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor, though the limited availability of skilled personnel may also impair its implementation in Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures is hindering the development of new Mediterranean agricultural varieties and will negatively affect future competitiveness of the agricultural sector. We provide an overview of the heterogeneous panorama of phenotyping within Mediterranean countries, describing the state of the art of agricultural production, breeding initiatives, and phenotyping capabilities in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify strategies to overcome barriers and maximize the benefits of phenotyping and modeling approaches to Mediterranean agriculture and related sustainabilityinfo:eu-repo/semantics/publishedVersio

Measurement of plant growth in view of an integrative analysis of regulatory networks

As the regulatory networks of growth at the cellular level are elucidated at a fast pace, their complexity is not reduced; on the contrary, the tissue, organ and even whole-plant level affect cell proliferation and expansion by means of development-induced and environment-induced signaling events in growth regulatory processes. Measurement of growth across different levels aids in gaining a mechanistic understanding of growth, and in defining the spatial and temporal resolution of sampling strategies for molecular analyses in the model Arabidopsis thaliana and increasingly also in crop species. The latter claim their place at the forefront of plant research, since global issues and future needs drive the translation from laboratory model-acquired knowledge of growth processes to improvements in crop productivity in field conditions

Monitoring drought responses of barley genotypes with semi-robotic phenotyping platform and association analysis between recorded traits and allelic variants of some stress genes

Genetic improvement of complex traits such as drought adaptation can be advanced by the combination of genomic and phenomic approaches. Semi-robotic phenotyping platform was used for computer-controlled watering, digital and thermal imaging of barley plants grown in greenhouse. The tested barley variants showed 0–76% reduction in green pixel-based shoot surface area in soil with 20% water content, compared to well-watered plants grown in soil with 60% water content. The barley HvA1 gene encoding the group 3 LEA (Late Embryogenesis Abundant) protein exhibited four (A–D) haplotypes as identified by the EcoTILLING and subsequent DNA sequencing. The green pixel mean value of genotypes with haplotype D was higher than the mean value of the remaining haplotypes, indicating a pivotal role of haplotype D in optimizing the green biomass production under drought condition. In water limitation, the canopy temperature of a highly sensitive genotype was 18.0°C, as opposed to 16.9°C of leaves from a tolerant genotype as measured by thermal imaging. Drought-induced changes in leaf temperature showed moderate correlation with the water use efficiency (r2 = 0.431). The haplotype/trait association analysis based on the t-test has revealed a positive effect of a haplotype B (SNPs:GCCCCTGC) in a gene encoding the barley fungal pathogen induced mRNA for pathogen-related protein (HvPPRPX), on harvest index, thousand grain weight, water use efficiency and grain yield. The presented pilot study established a basic methodology for the integrated use of phenotyping and haplotyping data in characterization of genotype-dependent drought responses in barley