Measurement of sap flow dynamics through the tomato peduncle using a non-invasive sensor based on the heat field deformation method

Recent contradicting evidence on the contributions of xylem and phloem to tomato fruit growth highlights the need for a more thorough insight into the dynamics of sap flow through the tomato peduncle. In fact, knowledge on sap flow dynamics through small plant parts remains scarce, due to a lack of direct measurements. Most currently available sap flow sensors use needles, making them inappropriate for the direct measurement of sap flow through small plant parts such as a tomato peduncle. Therefore, a non-invasive sap flow sensor based on the heat field deformation (HFD) principle was tested on the peduncle of a tomato truss. This mini HFD sensor, consisting of a heater element and three thermocouples stitched on insulation tape, was wrapped around the peduncle and allowed continuous monitoring of changes in the heat field around the heater caused by sap flow. Actual influx into the tomato truss was calculated based on fruit growth data and estimates of fruit transpiration and was compared with the dynamics measured with the mini HFD sensor. Additionally, heat girdling of the peduncle was performed to block phloem influx to study the dynamics of xylem and phloem influx using the mini HFD sensor. First results of the mini HFD sensor were promising and the measured sap flow dynamics through the tomato peduncle agreed well with the calculated sap influx. Results of the girdling experiment suggested opposite patterns of xylem and phloem influx, with a decreased xylem influx during the daytime. Furthermore, the pattern of xylem influx revealed a close relation with the total water potential in the stem. As such, the mini HFD sensor provided direct measurements of sap flow dynamics through a tomato peduncle and, hence, has a large potential to finally resolve the controversy on water influx into developing fruits

A flexible geometric model for leaf shape descriptions with high accuracy

Accurate assessment of canopy structure is crucial in studying plant-environment interactions. The advancement of functional-structural plant models (FSPM), which incorporate the 3D structure of individual plants, increases the need for a method for accurate mathematical descriptions of leaf shape. A model was developed as an improvement of an existing leaf shape algorithm to describe a large variety of leaf shapes. Modelling accuracy was evaluated using a spatial segmentation method and shape differences were assessed using principal component analysis (PCA) on the optimised parameters. Furthermore, a method is presented to calculate the mean shape of a dataset, intended for obtaining a representative shape for modelling purposes. The presented model is able to accurately capture a large range of single, entire leaf shapes. PCA illustrated the interpretability of the parameter values and allowed evaluation of shape differences. The model parameters allow straightforward digital reconstruction of leaf shapes for modelling purposes such as FSPMs

A decision support system for tomato growers based on plant responses and energy consumption

The importance of plant water status for a good production and quality of tomato fruits (Solanum lycopersicum L.) has been emphasized by many authors. Currently, different new energy-saving technologies and growing strategies are under investigation to cope with the increasing fossil fuel prices. However, these technologies and growing strategies typically alter the greenhouse climate, thereby affecting the plants' response. Hence, the question arises how to adapt the microclimate to reduce the energy consumption of greenhouse tomato cultivation without compromising fruit yield or quality. Nowadays, the use of plant-based methods to steer the climate is of high interest and it was demonstrated that monitoring of stem diameter variations and fruit growth provides crucial information on both the plant water and carbon status. However, interpretation of these data is not straightforward and, hence, mechanistic modelling is necessary for an unambiguous interpretation of the dynamic plant response. During a 4-year research period, we investigated the response of different plant processes of tomato to dynamic microclimatic greenhouse conditions. The final aim was to develop a decision support system that helps growers to find an optimal balance between energy consumption, plant response and fruit yield. To this end, an integrated plant model, including stem, leaves, roots and fruits, was developed in which the various plant processes are mechanistically described. The plant model was calibrated and extensively validated on datasets collected throughout the different growing seasons in different research facilities in Flanders. This plant model was finally integrated into an existing greenhouse climate model and validated with data from the greenhouse climate and energy consumption. After validation, this integrated model was used to run scenarios on growing strategies and their impact on energy consumption, plant photosynthesis and fruit growth

Effect of stem age on the response of stem diameter variations to plant water status in tomato

Plant water status plays a major role in glasshouse cultivation of tomato (Solanum lycopersicum L.). New climate control technologies alter the glasshouse climate and make it less dependent on solar radiation. However, irrigation strategies are still often based on solar radiation sums. In order to maintain a good plant water status, it is interesting to use plant-based methods such as monitoring sap flow (F) or stem diameter variations (SDV). Though SDV give important information about plant water status, an unambiguous interpretation might be difficult because other factors such as stem age, fruit load and sugar content of the stem also affect SDV. In this study, an analysis of the effect of stem age on the response of SDV to water status was performed by calibration of a mechanistic flow and storage model. This allowed us to determine how parameter values changed across the growing season. Tissue extensibility decreased over the growing season resulting in a lower growth rate potential, whereas daily cycles of shrinking and swelling of the stem became more pronounced towards the end of the growing season. Parameters were then adapted to time-dependent variables and implemented in the model, allowing long term simulation and interpretation of SDV. Sensitivity analysis showed that model predictions were very sensitive to initial sucrose content of the phloem tissue and the parameters related to plastic growth

Gloxinia—an open-source sensing platform to monitor the dynamic responses of plants

The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations

Limitations of snapshot hyperspectral cameras to monitor plant response dynamics in stress-free conditions

Plants' dynamic eco-physiological responses are vital to their productivity in continuously fluctuating conditions, such as those in agricultural fields. However, it is currently still very difficult to capture these responses at the field scale for phenotyping purposes. Advanced hyperspectral imaging tools are increasingly used in phenotyping, and have been applied to detect changes in plants in response to a specific treatment, phenological state or monitor its growth and development. Phenotyping has to evolve towards capturing dynamic behaviour under more subtle fluctuations in environmental conditions, without the presence of clear treatments or stresses. Therefore, we investigated the potential of hyperspectral imaging to capture dynamic behaviour of plants in stress-free conditions at a temporal resolution of seconds. Two growth chamber experiments were set up, in which strawberry plants and four different background materials, serving as controls, were monitored by a snapshot hyperspectral camera in variable conditions of light, temperature and relative humidity. The sampling period was set to three seconds, triggering image acquisition and gas exchange measurements. Different background materials were used to assess the influence of the environment and the camera in both experiments. To separate the plant and background data, static masks were determined. Two datasets were created, which encompass both experiments. One dataset was constructed after averaging over the entire mask to acquire one value per spectral band. These values were then used to calculate a set of vegetation indices. The other dataset used spatial subsampling to retain spatial information. From both datasets, linear models were constructed using ridge regression, which estimated the measured eco-physiological and environmental data. Leaf temperature and vapour pressure deficit based on leaf temperature are the two main eco-physiological characteristics that could be predicted successfully. Stomatal conductance, photosynthesis and transpiration rate show less promising results. We suspect that limited variation, and low spectral resolution and range are the main causes of the inability of the models to extract meaningful predictions. Furthermore, the models that were only trained on background data also showed good predictive performance. This is probably because the main drivers for good performing eco-physiological variables are temperature and incident light intensity. Environmental characteristics that have good performance are photosynthetically active radiation and air temperature. Current hyperspectral sensing technologies are not yet able to uncover most plant dynamic eco-physiological responses when plants are cultivated in stress-free conditions

Applying RGB- and thermal-based vegetation indices from UAVs for high-throughput field phenotyping of drought tolerance in forage grasses

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant

Linking stem diameter variations to sap flow, turgor and water potential in tomato

Water status plays an important role for fruit quality and quantity in tomato (Solanum lycopersicum L.). However, determination of the plant water status via measurements of sap flow (F-H2O) or stem diameter (D) cannot be done unambiguously since these variables are influenced by other effectors than the water status. We performed a semi-seasonal and a diurnal analysis of the simultaneous response of F-H2O and D to environmental conditions, which allowed us to distinguish different influences on Delta D such as plant age, fruit load and water status and to reveal close diurnal relationships between F-H2O and Delta D. In addition, an analysis of the diurnal mechanistic link between both variables was done by applying a slightly modified version of a water flow and storage model for trees. Tomato stems, in contrast with trees, seemed to maintain growth while transpiring because a large difference between turgor pressure (Psi(p)) and the yield threshold (Gamma) was maintained. Finally, the simultaneous response of D and F-H2O on irrigation events showed a possibility to detect water shortages