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

Spatial development of transport structures in apple (Malus x domestica Borkh.) fruit

The void network and vascular system are important pathways for the transport of gases, water and solutes in apple fruit (Malus x domestica Borkh). Here we used X-ray micro-tomography at various spatial resolutions to investigate the growth of these transport structures in 3D during fruit development of ‘Jonagold’ apple. The size of the void space and porosity in the cortex tissue increased considerably. In the core tissue, the porosity was consistently lower, and seemed to decrease towards the end of the maturation period. The voids in the core were more narrow and fragmented than the voids in the cortex. Both the void network in the core and in the cortex changed significantly in terms of void morphology. An automated segmentation protocol underestimated the total vasculature length by 9 to 12% in comparison to manually processed images. Vascular networks increased in length from a total of 5 meter at 9 weeks after full bloom, to more than 20 meter corresponding to 5 cm of vascular tissue per cubic centimeter of apple tissue. A high degree of branching in both the void network and vascular system and a complex three-dimensional pattern was observed across the whole fruit. The 3D visualisations of the transport structures may be useful for numerical modeling of organ growth and transport processes in fruit

Field-based Robot Phenotyping of Sorghum Plant Architecture using Stereo Vision

Sorghum (Sorghum bicolor) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops

Maize and sorghum plant detection at early growth stages using proximity laser and time-of-flight sensors

Maize and sorghum are important cereal crops in the world. To increase the maize grain yield, two approaches are used: exploring hybrid maize in plant breeding and improving the crop management system. Plant population is a parameter for calculating the germination rate, which is an important phenotypic trait of seeds. An automated way to obtain the plant population at early growth stages can help breeders to save measuring time in the field and increase the efficiency of their breeding programs. Similar to what has been taking place in production agriculture, plant scientists and plant breeders have been looking for and adopting precision technologies into their research programs; and analyzing plant performance plot-by-plot and even plant-by-plant is becoming the norm and vitally important plant phenomics research and seed industry. Accurate plant location information is needed for determining plant distribution and generating plant stand maps. Two automated plant population detection and location estimation systems using different sensors were developed in this research. A 2D machine vision technique was applied to develop a real-time automatic plant population estimation and plant stand map generation system for maize and sorghum in early growth stages. Laser sensors were chosen as they are not affected by outdoor lighting conditions. Plant detection algorithms were developed based on the unique plant stem structure. Since maize and sorghum look similar at early growth stages, the system was tested over both plants in greenhouse condition. The detection rate of over 93.1% and 83.0% were achieved for maize and sorghum plants from V2 to V6 growth stage, respectively. The mean absolute error and root-mean-error of plant location were 3.1 cm and 3.2 cm m for maize and 2.8 cm and 2.9 cm for grain sorghum plants, respectively. Apart from using laser sensors, a 3D Time-of-Flight camera-based automatic system was also developed for maize and sorghum plant detection at their early growth stages. The images were captured by using a Swift camera from a side-view of the crop row without any shade during the daytime in a greenhouse. A serious of image processing algorithms including point cloud filtering, plant candidate extraction, invalid plant removal, and plant registration were developed for this system. By comparing with the manual measurement, for the maize plant, the average true positive detection rate was 89% with 0.06 standard deviation. For grain sorghum plants, the average true positive detection rate was 85% with 0.08 standard deviation

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

Developing affordable high-throughput plant phenotyping methods for breeding of cereals and tuber crops

High-throughput plant phenotyping (HTPP) is a fast, accurate, and non-destructive process for evaluating plants' health and environmental adaptability. HTPP accelerates the identification of agronomic traits of interest, eliminates subjectivism (which is innate to humans), and facilitates the development of adapted genotypes. Current HTPP methods often rely on imaging sensors and computer vision both in the field and under controlled (indoor) conditions. However, their use is limited by the costs and complexity of the necessary instrumentation, data analysis tools, and software. This issue could be overcome by developing more cost-efficient and user-friendly methods that let breeders, farmers, and stakeholders access the benefits of HTPP. To assist such efforts, this thesis presents an ensemble of dedicated affordable phenotyping methods using RGB imaging for a range of key applications under controlled conditions.  The affordable Phenocave imaging system for use in controlled conditions was developed to facilitate studies on the effects of abiotic stresses by gathering data on important plant characteristics related to growth, yield, and adaptation to growing conditions and cultivation systems. Phenocave supports imaging sensors including visible (RGB), spectroscopic (multispectral and hyperspectral), and thermal imaging. Additionally, a pipeline for RGB image analysis was implemented as a plugin for the free and easy-to-use software ImageJ. This plugin has since proven to be an accurate alternative to conventional measurements that produces highly reproducible results. A subsequent study was conducted to evaluate the effects of heat and drought stress on plant growth and grain nutrient composition in wheat, an important staple cereal in Sweden. The effects of stress on plant growth were evaluated using image analysis, while stress-induced changes in the abundance of key plant compounds were evaluated by analyzing the nutrient composition of grains via chromatography. This led to the discovery of genotypes whose harvest quality remains stable under heat and drought stress. The next objective was to evaluate biotic stress; for this case, the effect of the fungal disease Fusarium head blight (FHB) that affects grain development in wheat was investigated. For this purpose, seed phenotyping parameters were used to determine the components and settings of a statistical model, which predicts the occurrence of FHB. The results reveal that grain morphology evaluations, such as length and width, were found to be significantly affected by the disease. Another study was carried out to estimate the disease severity of the common scab (CS) in potatoes, a widely popular food source. CS occurs on the tubers and reduces their visual appeal, significantly affecting their market value. Tubers were analyzed by a deep learning-based method to estimate disease lesion areas caused by CS. Results showed a high correlation between the predictions and expert visual scorings of the disease and proved to be a potential tool for the selection of genotypes that fulfill the market standards and resistance to CS. Both case studies highlight the role of imaging in plant health monitoring and its integration into the larger picture of plant health management.  The methods presented in this work are a starting point for bridging the gap between costs and accessibility to imaging technology. These are affordable and user-friendly resources for generating pivotal knowledge on plant development and genotype selection. In the future, image acquisition of all the methods can be integrated into the Phenocave system, potentially allowing for a more automated and efficient plant health monitoring process, leading to the identification of tolerant genotypes to biotic and abiotic stresses

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

LiDARPheno: A Low-Cost LiDAR-based 3D Scanning System for Plant Morphological Trait Characterization

The ever-growing world population brings the challenge for food security in the current world. The gene modification tools have opened a new era for fast-paced research on new crop identification and development. However, the bottleneck in the plant phenotyping technology restricts the alignment in Geno-pheno development as phenotyping is the key for the identification of potential crop for improved yield and resistance to the changing environment. Various attempts to making the plant phenotyping a “high-throughput” have been made while utilizing the existing sensors and technology. However, the demand for ‘good’ phenotypic information for linkage to the genome in understanding the gene-environment interactions is still a bottleneck in the plant phenotyping technologies. Moreover, the available technologies and instruments are inaccessible, expensive and sometimes bulky. This thesis work attempts to address some of the critical problems, such as exploration and development of a low-cost LiDAR-based platform for phenotyping the plants in-lab and in-field. A low-cost LiDAR-based system design, LiDARPheno, is introduced in this thesis work to assess the feasibility of the inexpensive LiDAR sensor in the leaf trait (length, width, and area) extraction. A detailed design of the LiDARPheno, based on low-cost and off-the-shelf components and modules, is presented. Moreover, the design of the firmware to control the hardware setup of the system and the user-level python-based script for data acquisition is proposed. The software part of the system utilizes the publicly available libraries and Application Programming Interfaces (APIs), making it easy to implement the system by a non-technical user. The LiDAR data analysis methods are presented, and algorithms for processing the data and extracting the leaf traits are developed. The processing includes conversion, cleaning/filtering, segmentation and trait extraction from the LiDAR data. Experiments on indoor plants and canola plants were performed for the development and validation of the methods for estimation of the leaf traits. The results of the LiDARPheno based trait extraction are compared with the SICK LMS400 (a commercial 2D LiDAR) to assess the performance of the developed system. Experimental results show a fair agreement between the developed system and a commercial LiDAR system. Moreover, the results are compared with the acquired ground truth as well as the commercial LiDAR system. The LiDARPheno can provide access to the inexpensive LiDAR-based scanning and open the opportunities for future exploration