Root hair counting is a specialized aspect of plant biology and agronomy research that offers valuable insights into plant health, nutrient uptake, and overall growth potential. Root hairs are tiny extensions from the root epidermis that significantly increase the surface area and constitute roughly 70% of the total root area of a plant root system, enhancing the ability of plants to absorb water and nutrients from the soil. Understanding the importance of root hair counting involves looking at various aspects of plant physiology and soil–plant interactions. Despite these benefits, counting root hairs, especially manually, can be tedious, time-consuming, and, more often, inaccurate due to differences in the perception of individuals. Therefore, we have proposed a novel method for root hair counting and further observed an improvement in root hair count measurements when utilizing image super-resolution as a preprocessing step. Our approach of counting root hairs can tackle real-world challenges and be able to count overlapping hairs as well. By visualizing the rhizosphere in binary space, we can see a considerable increase in root hair count from 37 to 68 when counting manually to our approach for Bell pepper, and from 44 to 88, when counting manually to our method for Arabidopsis root images. To the best of the authors’ knowledge, this research study is specifically designed for root hair counting and measurement improvement using super-resolution, is the first of its kind, and has yet to be acknowledged.

Roots, as the hidden half of plants, are the main organ absorbing water and nutrients from the soil. Yet, research into plant roots has lagged behind investigations of aboveground plant organs due to the difficulty of continuous monitoring of phenotypic changes in root architecture underground in a non-destructive manner. In this study, we developed a novel minirhizotron system based on common components of the fluorescence microscope. We examined the possibility of a pilot system for imaging green fluorescent protein (GFP) expression in roots within rhizoslides and glass containers and tested different parameters in order to achieve the best fit for imaging. Our results demonstrate that imaging GFP expression in roots provides a clearer visualization of the root system, effectively increasing an observable number of roots by minimizing interference from the soil compared to RGB images. We further miniaturized the imaging system and integrated it into the minirhizotron. The developed fluorescence minirhizotron is fully automated, high-throughput, and non-invasive allowing us to detect clear, continuous, in situ GFP fluorescence in roots. It is applicable across a wide range of scenarios. Currently, our ongoing work focuses on producing stress-inducible GFP expression in transgenic tobacco lines to enable rapid and early detection of plants under stress in a non-destructive manner. This study could help in distinguishing the roots of different plants and provide a potential contribution to breeding plants or in developing agro-techniques to save water, increase nutrient uptake, and improve crop yields in the era of climate change.

Introduction:The root system plays a key role in plant nutrient and water uptake, influencing growth, yield, and nitrogen use efficiency (NUE). However, excessive N fertilizer is often applied to boost productivity but can instead reduce efficiency and cause nitrate leaching, leading to underground water pollution. Optimizing N fertilization requires an understanding of root system response to nitrogen. Methods:We examined the effect of different N rates on root growth using in situ minirhizotron (MR) and destructive root study methods (ingrowth core, soil core, and soil excavation). In a net-house experiment, bell peppers (Capsicum annuum) were examined under different N concentrations: 100 ppm (control), 50 ppm (moderate-N), and 25 ppm (low-N). Results:Reduced N concentrations reduced leaf gas exchange and shoot biomass, but promoted root growth. Across all four methods, root length density (RLD), total root length (TRL), and root surface area significantly increased under reduced N. RLD values in the upper 30 cm of the soil profile were significantly higher under the low and moderate-N treatments compared to the control treatment, while fine roots (<2 mm thickness) exhibited increased RLD with low-N treatment. MR system recorded a higher RLD of ~70% and 33% compared to ingrowth core and excavation, respectively, likely due to loss of fine roots during washing. Discussion:Our findings indicate that while reduced N application significantly enhanced root growth, resource allocation varied between low and moderate-N treatments. The moderate-N treatment achieved a balance, supporting both increased root development and yield. In contrast, the low-N treatment enhanced root growth and NUE but did not translate into higher yield. This suggests that N-induced root system plasticity is critical in optimizing nutrient uptake efficiency and ensuring balanced resource allocation for both root and shoot development, as demonstrated by the moderate-N treatment.

Understanding plant root systems is critical for advancing research in soil-plant interactions, nutrient uptake, and overall plant health. However, accurate imaging of roots in subterranean environments remains a persistent challenge due to adverse conditions such as occlusion, varying soil moisture, and inherently low contrast, which limit the effectiveness of conventional vision-based approaches. In this work, we propose a novel underground imaging system that captures multiple overlapping views of plant roots and integrates a deep learning-based Multi-Image Super Resolution (MISR) framework designed to enhance root visibility and detail. To train and evaluate our approach, we construct a synthetic dataset that simulates realistic underground imaging scenarios, incorporating key environmental factors that affect image quality. Our proposed MISR algorithm leverages spatial redundancy across views to reconstruct high-resolution images with improved structural fidelity and visual clarity. Quantitative evaluations show that our approach outperforms state-of-the-art super resolution baselines, achieving a 2.3 percent reduction in BRISQUE, indicating improved image quality with the same CLIP-IQA score, thereby enabling enhanced phenotypic analysis of root systems. This, in turn, facilitates accurate estimation of critical root traits, including root hair count and root hair density. The proposed framework presents a promising direction for robust automatic underground plant root imaging and trait quantification for agricultural and ecological research.

Primary productivity in arid zones is limited by the lack of water and soil nutrients. Conveying and storing flood water in plots surrounded by embankments allows agricultural activity in areas where there is generally insufficient rainfall to sustain agricultural production. The efficient exploitation of the stored water was achieved by intercropping trees with an annual crop and pruning the former before planting the intercrop. This approach minimized competition for water and solar radiation. However, in order to ensure the long-term viability of such a system nutrients have to be added to the soil in order to compensate for the uptake of the intercrop, Nitrogen being the main element. The composted leaves of a leguminous shrub-like tree incorporated into the soil could satisfy the nitrogen demand of the intercrop. We tested this approach in a simulated runoff agroforestry system with fast-growing acacia (A. saligna) trees as the woody component and maize (Zea mays L.) as intercrop for two consecutive seasons. Ten treatments were applied (radical pruning before intercrop planting, compost application and planting of the intercrop as factors) and the below- and above-ground effects and interactions examined. Pruning the trees canopies changed the trees’ root spatial and temporal distribution, allowing the annual crop to develop between the trees. Addition of compost significantly increased intercrop yield irrespective of the presence of the woody component while the presence of the intercrop did not affect the productivity of the trees. The highest productivity was obtained for the pruned trees, intercrop and added compost treatment. A significant increase in the presence of tree roots was observed for the deeper parts of the soil profile for the pruned trees, intercrop and added compost treatment. The addition of composted leaves from the leguminous woody component to the intercrop resulted in a very high water use efficiency of the water stored in the soil.

Root hairs are essential for nutrient uptake and plant-microbe interactions, playing a vital role in plant health and agricultural productivity. They extend from the surface of root cells, significantly increasing the root surface area, and constitute roughly 70% of the total root area. Given these advantages, detecting root hairs in scenes with low resolution is challenging. Therefore, we have proposed a study that utilizes unsupervised image super-resolution methods to reconstruct finer details for root hairs using the dataset captured from our novel scanning camera known as RootCam. RootCam is a fully automated tool designed for monitoring and capturing plant root images for different vision tasks for a more accurate representation of their morphology and root trait measurements. Root hair super-resolution proves to be a powerful tool for root biology and its applications in precision agriculture. To the best of the authors' knowledge, this research study is the first that mainly focuses on root hairs and their trait measurement improvement using super-resolution. By visualizing the rhizosphere in high-resolution detail, we are able to notice a significant improvement in bell-pepper plant root hair count from 7 to 12, total root length from 0.32 to 1 mm, and root hair density (number of root hairs/mm) from 2.7 to 4.63, as upscaling factors rise from 2 to 8, respectively, when compared with bicubic and contrastive learning semi-supervised remote sensing image super (CLSR) for super-resolution. Researchers and farmers can make informed decisions about nutrient placement, irrigation management, and crop selection, optimizing resource use efficiency and crop yields.

Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination (R2), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models’ performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.

This work presents a framework based on convolutional neural networks (CNNs) to estimate root traits (length, diameter, and color) from minirhizotron (MR) imagery. The proposed framework uses a set of reusable sub-network modules to compose different networks for object (i.e., root) detection and attribute (i.e., trait) estimation for per-root and per-image root phenotyping tasks. It provides a solution without requiring root segmentation. The first step in per-root phenotyping involves detecting the roots in the image; the traits of each detected root are then estimated. Per-image root phenotyping estimates aggregated root trait values, including total root length (TRL), mean root diameter, and percentage of white root. Regression-based and objects' points-detection-based variations are demonstrated for both per-root and per-image root trait estimation. Five network architectures are presented, two of which were previously used for TRL estimation (and are now evaluated for estimating mean root diameter and white root percentage), and three of which are new. The proposed framework is demonstrated on an annotated grapevine root dataset comprising 531 images, made publicly available as part of this paper. All images were acquired in situ using an MR system and annotated with Rootfly software. Regression-based modules used for individual detected roots yielded errors of 8.8%, 15.5%, and 23.5% for color, length, and diameter, respectively. The points-detection-based modules resulted in errors of 9.1%, 14.9%, and 25.0% for the same parameters. The image-level estimates showed errors of 11.5%-16.5% for white root percentage, 13.7%-16.0% for TRL, and 17.6%-22.1% for mean root diameter. We demonstrate that aggregating per-root estimations of diameter and color obtained with the new suggested architectures improves the per-image estimations of these traits relative to the direct per-image estimation that does not include per-root estimations. To demonstrate further the practicality of the suggested framework in deriving the vertical distribution of various root traits, an additional dataset of 132 root images from two different grapevine graft combinations was annotated (and also made publicly available as part of this paper). In this dataset, the per-image root traits were estimated for different soil depths and visually compared with human annotation results.

Arid environments are characterized by rare rain events that are highly variable, as a result of which plant populations often exhibit episodic recruitment and mortality dynamics. However, direct records and observations of such events are rare because of the slow development of woody species. In this study, we described how a decrease in annual precipitation affected acacia tree population dynamics in two hydrological regime types: small wadis and salt flats. This study combines 15 years of continuous, yearly field monitoring of individual acacia trees and data from a historical Corona satellite image, which has extended the time scope of the research. Results indicate that the annual mortality of acacia trees in small wadis reflects the cumulative effective rain events in the preceding five years, whereas the population on the salt flats was not affected by annual rainfall fluctuations. Moreover, in small wadis, rain events of less than 8 mm did not increase acacia tree survival rates. The mortality pattern and dynamics of each plot was unique, suggesting unsynchronized mortality and recruitment episodes on a regional scale. Mortality in all plots was documented both in “old” trees (i.e., recognized in 1968) and “new” trees (not recognized in 1968), but varied highly between plots. More than 50% of the dead trees recorded at the sites had died during the previous dry period (2000–2010). Combining field monitoring and historical satellite image data provided a unique database of acacia population dynamics. This record revealed the response of the acacia population to climate fluctuations and a period of episodic mortality.

Root phenotyping in the field remains challenging from root imaging to data analysis since each part of this process is time-consuming and labor-intensive. Extensive efforts have been taken to explore the possibility to automate parts of this process. However, few studies have provided an integrated solution to make the whole process in a manner of low cost, automated, and customizable for different tasks. In this study, we have worked towards this goal. A newly designed root imaging system called RootCam addresses the above-mentioned limitations. RootCam moves a small camera with fully automated operations for long-term in-situ monitoring. It captures high-resolution root images (2592 x 1944 pixels). These images are saved to a “Raspberry Pi” device which is accessible by a network cable allowing users to control the system remotely. Users can also control time intervals between runs and set image capturing either overlapped or non-overlapped. This camera was tested in a net house by imaging bell pepper roots which shows superior performance over commercial minirhizotron systems. A deep convolutional neural networks (CNN) model was developed to detect plant roots and calculate root length. This model was trained and calibrated with a dataset of ~18,000 tomato root images and has been used for calculating bell pepper root length on 832 images. The high correlation coefficient (R2 = 0.854) between the measurements from the automated and manual methods proved that our model is able to generalize well over different crop roots. However, the model underestimates root length when there are many roots in an individual image. In summary, the platform we developed to automatize minirhizotron image acquisition and analysis has the promising potential to benefit both the root research community via accelerating high throughput root phenotyping in the field for root studies and farmers via making real-time root development information available for decision making.

Minirhizotron technique (MR), an image-based root phenotyping technology, has expanded our understanding of in situ root responses to changing environmental conditions. However, the conventional manual approaches to capturing and analyzing images are time-consuming, thus constraining the size and frequency of sampling and interpretation. To address this bottleneck, an automated MR system was developed, which worked in a fully automatic manner according to the pre-set schedule. Users can remotely control the system and download images. This system was tested in a net house by imaging bell pepper roots on a daily basis, which shows superior performance over commercial manual MR systems in terms of image resolution and sampling frequency. Besides, an image analysis model was built on convolutional neural networks to estimate root length from MR images directly without segmentation in the training process. This model was trained on a dataset of ~18,000 tomato root images taken by a manual MR camera and was used for estimating root length on 832 bell pepper root images taken by the automated MR camera. The high correlation coefficient (R = 0.854) between the model estimation and manual measurement proved that this model generalizes well over different crop roots and camera types. Therefore, high-frequency in situ root phenotyping can be achieved by the automated Minirhizotron image acquisition and analysis tools proposed here.

Grafting with different rootstocks may provide increased tolerance and yield, even under poor-quality irrigation conditions. We examined the effects of potato rootstock on physiology, dry mass, and yield of tomato scion in pots irrigated with saline water. Tomato (cv. Ikram), potato (cv. Charlotte) and grafted (cv. Ikram/Charlotte) plants were subjected to saline and non-saline water-irrigation treatments (electrical conductivity 5.0 and 1.0 dS m−1, respectively). Physiological, mineral, dry mass and yield analyses were performed. Potato rootstock unchanged the total plant dry mass without disturbing the physiology of the tomato scion under saline water irrigation. The grafted plants showed differential root trait responses with balanced mineral partitioning across plant parts under saline water irrigation. Grafted plants were superior in water productivity by 56.8 and 70.5 % over the control plants under saline and non-saline water-irrigations, respectively. Potato rootstock could improve the tolerance of tomato scion to saline water irrigation through distinct changes in dry mass allocation, and the induction of mineral-compartmentalization processes. The results of this study suggest that the use of potato rootstock may be a good strategy for increasing tolerance to saline water irrigation, as well as the production of both fruits and tubers in a single plant.

Container size and fruit load intensity are two common factors manipulated to regulate plant growth and development. As saline water is increasingly used for irrigation in arid and semi-arid regions, it is important to study effects of container size and fruit load intensity on tomato in both aboveground and belowground parts under salt stress. The experiment was conducted in a net house located in Sede Boqer Campus, Israel. Containers of four sizes (8-, 28-, 48-, and 200L with the same depth but vary in diameters), two salinity levels (1.5- and 7.5 dS m−1) and two crop load intensities (0% and 100%) were applied. Gas exchange parameters (i.e., stomatal conductance and CO2 assimilation rate), plant growth parameters (i.e., plant height and stem diameter), and root development were monitored periodically. Plant biomass and various root traits were measured at harvest. For aboveground part, results revealed that container size and salinity level significantly influenced gas exchange performance while fruit load intensity had no significant effect. Plants grown in larger containers without salt stress had higher stomatal conductance and CO2 assimilation rate. Plant height and stem diameter were significantly greater in plants grown in 200L than those in other containers despite salinity and fruit load levels. Moreover, plants grown in 200L containers exhibited significant increase of 56.3%, 152.9%, and 174.9% respectively in yield compared with those grown in 48-, 28- and 8L under salt stress. The increase magnitudes were greater when there was no salt stress: 109.0%, 430.8%, and 454.0% respectively. For belowground parts, increased container size leads to increased rooting depth. Besides, Minirhizotron data showed that in 200L containers, plants grown under low salinity without fruit developed the greatest total root length. More detailed root data will be presented. It is concluded that container size has a pronounced effect on physiological behaviours of tomato plants. Therefore, properly increasing container size can alleviate yield reduction under saline irrigation.

Understanding the spectral characteristics of crops in response to stress caused by weeds is a basic step in improving the precision of agricultural technologies that manage weeds in the field. This research focused on the competition between corn (Zea mays) and redroot pigweed (Amaranthus retroflexus), a common weed that strongly reduces corn yield. The aim of this research was to characterize the physiological changes that occur in corn during early growth because of crop–weed competition and to examine the ability to detect the effect of competition through hyperspectral measurements. A greenhouse experiment was conducted, and corn plants were examined during early growth, with and without weed competition. Hyperspectral measurements were combined with physiological measurements to examine the reflectance and photosynthetic activity of corn. Changes were expected to appear mainly in the short-wave infrared region (SWIR) due to competition for water. Relative water content (RWC), chlorophyll content, photosynthetic rate, and stomatal conductance were reduced in the presence of weeds, and intercellular CO2 levels increased. Deeper SWIR light absorption occurred in the weed treatment as expected, accompanied by spectral changes in the visible (VIS) and near infrared (NIR) ranges. The results highlight the potential of using spectral measurements as an indicator of competition for water.

Weed-crop competition is a problem affecting food production leading to significant yield losses in various crops. The use of remote sensing technologies in agriculture enables rapid, non-destructive measurements that can be used for research and agronomical management. Previous research has been conducted characterizing the spectral response of crops to the stress caused by weeds but not much progress has been achieved nor has this been fully connected to physiological processes. Understanding the spectral characteristics of this type of stress is a basic step in advancing precision agricultural technologies for managing weeds in the field. This research focuses on corn (Zea mays) with variable densities of redroot pigweed (Amaranthus retroflexus), a common weed that is known to reduce corn yields. The primary research goal is to characterize the physiological changes that occur in the corn during early growth stages in the presence of weeds of different densities. A secondary goal, is to examine the ability to detect those changes by means of proximal and remote sensing.During June to August 2019, a field experiment was conducted in Sede – Boker, Israel. Hyperspectral reflectance measurements using an ASD spectrometer,IR images acquired with a thermal camera and multispectral VIS-NIR images from a mounted UAV were taken. We combined the spectral measurements with physiological measurements (photosynthesis, stomatal conductance and transpiration). The data and results were integrated and analyzed to determine whether physiological differences between variable treatments can be detected by the sensing methods. Results show that these can be observed, detected and we will provide new explanations associating the competition, spectral response and physiological processes.

Salinity affects plant growth due to both osmotic and ionic stress. The root system is essential in defense mechanisms against salinity, particularly involving salt ion avoidance or exclusion. Jojoba (Simmondsia chinensis) displays significant resistance to salinity. In the present study, Jojoba was planted in 60-L plastic buckets containing perlite growth medium and were provided with eight distinct salinity levels using two operating tanks of final irrigation solutions. Response of Jojoba to salinity was measured in above ground parameters and in roots using minirhizotron access tubes and imaging analysis. Leaf phosphorous and potassium concentrations decreased with increasing salinity level while leaf manganese, calcium, sodium and chloride concentrations increased with irrigation salinity level. Jojoba plants were found to have high level of storage of salt minerals in leaves but without effects on photosynthesis or transpiration. Roots exhibited different distribution patterns under different salinity treatments. Root length density increased with increased salinity at each depth. Root number and root length increased over time. During spring, the plant growth was faster than winter. Root diameter decreased over time due to new root development. Time had a more significant effect on root length density than irrigation water salinity or soil depth. Root number and root length were not significantly affected by the salt treatments.

In the published version of this editorial paper, the sentence in the first paragraph should be corrected as shown below.

The use of minirhizotron (MR) imaging systems is gaining popularity, resulting in a large amount of collected images—which need efficient and accurate processing for root trait extraction. This study proposes a neural network-based solution for automatic measurement of root length in images taken by MR systems. Current root length measurement techniques involve two steps; manually operating the MR for taking the images, and manually annotating roots in front of a noisy rhizosphere ‘background’ with a dedicated software. As the analysing process is extremely time consuming, automation can both lower the costs and facilitate greater temporal resolution. Using convolutional neural networks (CNN) in image classification tasks has become very common due to its simplicity, yet regression tasks are still considered difficult. We propose a new model that combines the strength of conditional learning, transfer learning and bagging in order to achieve a precise regression. The dataset used holds 12,000 highly diverse images of 5 tomatoes cultivars, which were collected by a BARTZ minirhizotron camera over a period of 4 months. Initial results show a success rate of 75% accuracy with 33 mm Mean Absolute Error (MAE). Error analysis shows that large errors occur on images with either a very high or a low root length density. Additionally, a separate model was designed and tested on selected subsets of the data by using a synthetic data generator. Results show that MAE decreases to 10 mm, which is equivalent to 90% accuracy. Results suggest that this method has great potential to facilitate fully automatic root length measurement on noisy rhizosphere images. Future work will validate the proposed model with a larger datasets comprising of various plant species, soil types and MR imaging systems.

Core Ideas Shoot and root interactions under extreme temperatures contributed to tolerance. Impact of root dynamics on soil pore water EC and Cl uptake increased under extreme temperatures. Tolerance of extreme temperatures increased assimilation and allocation of C and N to organ growth. Fluctuations of winter and summer and day and night temperatures strongly influence shoot and root growth, as well as the whole plant tolerance to extreme soil temperatures. We compared the response of a commercial pepper (Capsicum annuum L.) hybrid (Romance, Rijk Zwaan) to a range of soil temperatures when grafted to a new rootstock hybrid (S101, Syngenta), self‐grafted, or ungrafted. The new rootstock hybrid was bred for enhancing abiotic stress tolerance. Plants were grown during winter and summer seasons in a plastic greenhouse with natural ventilation. Minirhizotron cameras and in‐growth cores were used to investigate grafted bell pepper root dynamics and root and shoot interactions in response to extreme (low and high air and soil) temperatures. Soil and air temperatures were measured throughout the experiment. The variations of the grafted peppers and the ungrafted aboveground biomass exposed to low and high temperatures during winter and summer were higher in the Romance grafted on the S101 rootstock than in the self‐grafted and ungrafted Romance. The plot of rootstock S101 accumulated Cl, and the rootstock efficiently allocated C into the leaves, stems, and roots and N into the leaves, stems, and fruits. These traits of rootstock S101 can be used to improve the tolerance of other pepper cultivars to low and high soil temperatures, which could lengthen the pepper growing season, as well as provide highly interesting information to plant breeders.