Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution

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

Leveraging Image Analysis for High-Throughput Plant Phenotyping

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant’s phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field

Close-range hyperspectral imaging of whole plants for digital phenotyping : recent applications and illumination correction approaches

Digital plant phenotyping is emerging as a key research domain at the interface of information technology and plant science. Digital phenotyping aims to deploy high-end non-destructive sensing techniques and information technology infrastructures to automate the extraction of both structural and physiological traits from plants under phenotyping experiments. One of the promising sensor technologies for plant phenotyping is hyperspectral imaging (HSI). The main benefit of utilising HSI compared to other imaging techniques is the possibility to extract simultaneously structural and physiological information on plants. The use of HSI for analysis of parts of plants, e.g. plucked leaves, has already been demonstrated. However, there are several significant challenges associated with the use of HSI for extraction of information from a whole plant, and hence this is an active area of research. These challenges are related to data processing after image acquisition. The hyperspectral data acquired of a plant suffers from variations in illumination owing to light scattering, shadowing of plant parts, multiple scattering and a complex combination of scattering and shadowing. The extent of these effects depends on the type of plants and their complex geometry. A range of approaches has been introduced to deal with these effects, however, no concrete approach is yet ready. In this article, we provide a comprehensive review of recent studies of close-range HSI of whole plants. Several studies have used HSI for plant analysis but were limited to imaging of leaves, which is considerably more straightforward than imaging of the whole plant, and thus do not relate to digital phenotyping. In this article, we discuss and compare the approaches used to deal with the effects of variation in illumination, which are an issue for imaging of whole plants. Furthermore, future possibilities to deal with these effects are also highlighted

Optimized Angles of the Swing Hyperspectral Imaging Tower for Single Corn Plant

During recent years, hyperspectral imaging systems have been widely applied in the greenhouses for plant phenotyping purposes. Current imaging systems are mostly designed as either top view or side view imaging mode. Top-view is an ideal imaging angle for top leaves which are often more flat with more uniform reflectance. However, most bottom leaves are either blocked or shaded from top view. From side view, most leaves are viewable, and the entire structure can be imaged. However, at this angle most of the leaves are not facing the camera, which will impact the measurement quality. At the same time, there could be advantages with certain tilted imaging angle between top view and side view. Therefore, it’s important to explore the impact of different imaging angles to the phenotyping quality. For this purpose, we designed a swing hyperspectral imaging tower which enables us to rotate the camera and lighting source to capture images at any angle from side view (0◦) to top view (90◦). 36 corn plants were grown and divided into 3 different treatments: high nitrogen (N) and well-watered (control group), high N and drought-stressed, and low N and well-watered. Each plant was imaged at 7 different angles from 0◦ to 90◦ with an interval of 15◦. According to different treatments applied on experimental samples, two comparative pairs were set up: drought-stressed group vs. control group (Pair 1); N-deficiency group vs. control group (Pair 2). In this study, normalized difference vegetation index (NDVI) and relative water content (RWC) were computed and compared to determine optimized imaging angle(s). For NDVI, the imaging angle near to top view is optimized to separate Pair 1, while, the imaging angle near to side view is optimized to distinguish Pair 2. For RWC, partial least square regression (PLSR) models were applied to predict pixel-level RWC distribution of each plant, and higher imaging angles (close to top view) are better to tell the RWC distribution difference in Pair 1. In conclusion, higher imaging angles (close to top view) are better to separate different water treatments, while, lower imaging angles (close to side view) are better to separate different N treatments

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

Potential phenotyping methodologies to assess inter- and intravarietal variability and to select grapevine genotypes tolerant to abiotic stress

ReviewPlant phenotyping is an emerging science that combines multiple methodologies and protocols to measure plant traits (e.g., growth, morphology, architecture, function, and composition) at multiple scales of organization. Manual phenotyping remains as a major bottleneck to the advance of plant and crop breeding. Such constraint fostered the development of high throughput plant phenotyping (HTPP), which is largely based on imaging approaches and automatized data retrieval and processing. Field phenotyping still poses major challenges and the progress of HTPP for field conditions can be relevant to support selection and breeding of grapevine. The aim of this review is to discuss potential and current methods to improve field phenotyping of grapevine to support characterization of inter- and intravarietal diversity. Vitis vinifera has a large genetic diversity that needs characterization, and the availability of methods to support selection of plant material (polyclonal or clonal) able to withstand abiotic stress is paramount. Besides being time consuming, complex and expensive, field experiments are also affected by heterogeneous and uncontrolled climate and soil conditions, mostly due to the large areas of the trials and to the high number of traits to be observed in a number of individuals ranging from hundreds to thousands. Therefore, adequate field experimental design and data gathering methodologies are crucial to obtain reliable data. Some of the major challenges posed to grapevine selection programs for tolerance to water and heat stress are described herein. Useful traits for selection and related field phenotyping methodologies are described and their adequacy for large scale screening is discussedinfo:eu-repo/semantics/publishedVersio

Early detection of drought stress in Arabidopsis thaliana utilsing a portable hyperspectral imaging setup

Close-range hyperspectral imaging (HSI) of plants is now a potential tool for non-destructive extraction of plant functional traits. A major motivation is the plant phenotyping related applications where different plant genotypes are explored for different environmental conditions. HSI of Arabidopsis thaliana is of particular importance as it is a model organism in plant biology. In the present work, a portable HSI setup has been used for the monitoring of a set of 6 Arabidopsis thaliana plants. The plants were monitored under controlled watering conditions where 3 plants were watered as normal and the other 3 plants were given 50% of the normal volume of water. The images were pre-processed utilising the standard normal variate (SNV) and changes over time were evaluated using unsupervised clustering over the time series. The results showed an early detection of stress from day 4 onwards compared to the commonly used normalised difference vegetation index (NDVI), which provided detection from day 9