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

Probiotics as biological control agent in sea bass larviculture

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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

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

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

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