Biology subjects

Averbuch, N.

Publications and source records attributed to Averbuch, N..

3 recordsLinked to original sources

bioRxiv2025

Wireless Sensor Network: New Concept of Spatial-Temporal Monitoring Plant-Environment Interactions

We present a low-cost, standards-based wireless sensor network (WSN) for continuous, canopy-integrated monitoring of plant-environment interactions. Each plant carries in-canopy microclimate sensors (temperature, relative humidity, illuminance) paired with nearby ambient references, yielding real-time canopy-ambient differentials. The system is easy to install: at planting or sowing, sensors are fixed at positions that will lie within the developing canopy, and a separate ambient reference area is designated and kept free of vegetation. As plants grow, they envelop the sensors, thereby capturing growth dynamics over time. The sensors accuracy was validated against a commercial weather station and portable system that measures gas exchange, temperature and light (LI-COR 6800/6400), and the systems ability to resolve plant physiological activity was confirmed using the PlantArray functional phenotyping platform with independent whole-plant transpiration and biomass references. Under controlled growth-room conditions and across two contrasting Cannabis cultivars, daily transpiration strongly predicted biomass gain (R{superscript 2} > 0.9). Microclimate signals mirrored physiology: midday canopy air was cooler by 4-7 {degrees}C, more humid by 18-25 % RH, and increasingly shaded as biomass accumulated, with temperature, RH, and light attenuation showing saturating logarithmic relationships with growth. The network operated for months unattended with low packet loss and predictable power use. It provides 4D (x-y-z-time) coverage, where x and y denote horizontal location, z the vertical position within the canopy, and time the dynamics, enabling resolution of where changes occur and how they evolve, and supplying high-frequency labeled data. This system complements, rather than replaces, precision instruments and high-end phenotyping platforms, providing a scalable layer for continuous tracking across wide areas. We outline practical constraints and next steps toward field pilots, modest energy harvesting, expanded sensor suites, and integration with machine learning for predictive crop management.

bioengineering↗

bioRxiv2024

Transpiration Insights: Estimating Transpiration Through Advanced Modeling

O_LIWe conducted research to predict daily transpiration in crops by utilizing a combination of machine learning (ML) models combined with extensive transpiration data from gravimetric load cells and ambient sensors. Our aim was to improve the accuracy of transpiration estimates. C_LIO_LIData were collected from hundreds of plant specimens growing in two semi-controlled greenhouses over seven years, automatically measuring key physiological traits (serves as our ground truth data) and meteorological variables with high temporal resolution and accuracy. We trained Decision tree, Random Forest, XGBoost, and Neural Network models on this dataset to predict daily transpiration. C_LIO_LIThe Random Forest and XGBoost models demonstrated high accuracy in predicting the whole plant transpiration, with R{superscript 2} values of 0.89 on the test set (cross-validation) and R2 = 0.82 on holdout experiments. Ambient temperature was identified as the most influential environmental factors affecting transpiration. C_LIO_LIOur results emphasize the potential of ML for precise water management in agriculture, and simplify some of the complex and dynamic environmental forces that shape transpiration. C_LI

plant biology↗

bioRxiv2020

A High Throughput Gravimetric Phenotyping Platform for Real Time Physiological Screening of Plant Environment Dynamic Responses

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating a knowledge gap. To meet the challenge of improving crops to feed the growing global population, this gap must be bridged. Physiological traits are considered key functional traits in the context of responsiveness or sensitivity to environmental conditions. Many recently introduced high-throughput phenotyping techniques are based on remote sensing or imaging and are capable of directly measuring morphological traits, but measure physiological parameters only indirectly. This paper describes a method for direct physiological phenotyping that has several advantages for the functional phenotyping of plant-environment interactions. It aims to help users overcome the many challenges encountered in the use of load-cell gravimetric systems and pot experiments. The suggested techniques will enable users to distinguish between soil weight, plant weight and soil water content, providing a method for continuous and simultaneous measurement of dynamic soil, plant and atmosphere conditions, alongside key physiological traits. This method allows researchers to closely mimic field stress scenarios while taking into consideration the environments effect on the plants physiology. This method also minimizes pot effects, which are one of the major problems in pre-field phenotyping. It includes a feedback fertigation system that enables a truly randomized experimental design with a field-like plant density. This system detects the soil-water content limiting threshold ({theta}) and allows for the translation of data into knowledge through the use of a real-time analytic tool and an online statistical resource. This method for the rapid and direct measurement of the physiological responses of multiple plants to a dynamic environment has great potential for use in screening for beneficial traits associated with responses to abiotic stress, in the context of pre-field breeding and crop improvement. SUMMARYThis high-throughput, whole-plant water relations gravimetric phenotyping method enables direct and simultaneous real-time measurements and analysis of multiple yield-related physiological traits involved in dynamic plant-environment interactions.

plant biology↗