Arbuscular mycorrhizal (AM) symbiosis is regulated by carotenoid-derived molecules, including strigolactones and other apocarotenoids. However, the role of root carotene availability remains poorly understood. Here we evaluated AM performance in tomato using two mutants showing contrasting root carotenoid profiles, i.e. cyc-b7, an EMS-derived TILLING mutant carrying a characterized mutation in Cyc-B, and 7458-Y, an uncharacterized mutant line showing high carotenoid accumulation), compared to their related wild type (Red Setter). Not-inoculated and AM fungal inoculated plants were grown under controlled conditions and root colonization parameters were assessed after two months. Root carotenoids were quantified by high-performance liquid chromatography (HPLC), and RNA-seq analysis was performed on root samples to understand the tomato response to the inoculation. Roots of cyc-b7 accumulated significantly lower total carotenoids than Red Setter, including reduced lutein and β-carotene, whereas 7458-Y showed increased β-carotene, together with higher arbuscule abundance than both Red Setter and cyc-b7. Transcriptome profiling revealed a genotype-dependent response to AMF inoculation, with symbiosis-related genes differentially regulated in the two mutant lines. In cyc-b7, AMF inoculation was associated with reduced expression of genes encoding nutrient transporters, as well as of a gene encoding a symbiosis receptor-like kinase (SYMRK), a component of the common symbiosis signaling pathway. By contrast, in 7458-Y, AMF inoculation was associated with up-regulation of a gene encoding a LysM receptor-like kinase involved in AM establishment, and of a gene, SlD27, related to strigolactone biosynthesis. Overall, our results support a link between root carotenoid metabolism and AMF colonization.

Early and accurate recognition of abiotic stress types is essential for accelerating the selection of stress-tolerant varieties and implementing effective management strategies. This study is motivated by the socio-economic relevance of vineyard and by the increasing need for stressor-specific fingerprint(s) to support the reliable identification of stress type (e.g., drought or salinity) within a high-throughput plant phenotyping domain.This paper presents a reanalysis of physiological and phenotyping data from drought and salt stress experiments in Vitis vinifera focussing the maximum photosynthetic efficiency (Fv/Fm) and leaf Dark Green color. The reanalysis suggests that salt-stressed vines might suffer additional (non-stomatal) limitations curbing net photosynthetic rate (Pn) severely as drought stress does at equivalent stomatal conductance (gs) levels. Through a Principal Component (PC) Analysis, physiological and colorimetric response variables were decomposed revealing that Fv/Fm and Dark Green dominates the non-stomatal PC (∼80 %) clustering data between salt and drought experiments. Confusion matrices reveal that model based on Fv/Fm and Dark Green performed better (accuracy = 1, precision =1) than that based on Pn, gs, transpiration, and stem water potential. This study supports the potential use of Fv/Fm and Dark Green for early and non-destructive stress type identification.

Ensuring crop yields in a world with a changing climate and increasing population is of utmost importance. To achieve this goal, genotypical and phenotypical techniques can be exploited to evaluate whether specific mutations prove to be more resilient to drought stress or extreme climate phenomena. This process can be further improved by providing domain experts with automated analytics tools to assist them in the analysis of a large amount of data, therefore providing more statistical reliability and robustness. To this end, this work proposes an image processing pipeline for helping practitioners in the automatic evaluation of the response of tomato plant mutations to drought stress. The pipeline is based on an efficient implementation of a colour space conversion, aiming at reducing the dependency on illumination of the a and b components of the CIELab colour space and providing statistical uniformity, and anisotropic Gaussian mixture model clustering for improved feature extraction and analysis. The results were evaluated over a total of 388 mutations in three time instants, highlighting the effectiveness of the workflow in supporting domain experts in assessing the most resilient tomato variants.

BACKGROUND AND AIMS:Phosphorus (P) is a crucial macronutrient for plant growth that, despite its abundance in soils, is often a limiting factor in agricultural productivity, particularly for cereals such as wheat. In this study, the response of different wheat genotypes to two different levels of P was evaluated in a large trial encompassing 26 genotypes using two distinct root phenotyping platforms, ALSIA and 4PMI. METHODS:Rhizotubes allowed non-invasive root phenotyping, revealing significant genotypic effects on biomass production and root system traits. Phosphorus acquisition and use efficiency of the wheat genotypes were estimated by using five different metrics. KEY RESULTS:A synthetic indicator for agronomic relevance integrating the efficiency metrics was established. Under optimal conditions, after 96 d, P acquisition efficiency (PAE) was inversely correlated with P utilization efficiency (PUE), suggesting an acquisition-use trade-off. Conversely, under low P conditions, both after 27 and 96 days PAE and PUE showed moderate positive correlations, indicating adaptive coordination to improve P utilization under scarcity. CONCLUSIONS:Overall, our findings highlighted the importance of root-target strategies in P efficiency in wheat, providing insights for breeders to enhance P deficiency tolerance in wheat.

This study explores the effects of natural seed priming compounds (i.e. chitosan alone and in combination with salicylic acid or melatonin) with the symbiosis of arbuscular mycorrhizal fungi (AMF) on the capability of two Italian tomato varieties (Principe Borghese and San Marzano nano) to withstand water deprivation through high-throughput plant phenotyping technology. Plant responses have been automatically evaluated by integrating physiological, morpho-biometric, and biochemical data. Under water deprivation, AMF-inoculated plants exhibited enhanced physiological performance, by reducing oxidative damage and improving stomatal function. Digital phenotyping provides a non-invasive approach to assess the effects of external factors, such as the impact of mycorrhizal fungi on plant development. RGB (visible light) imaging enables the analysis of morphological traits like plant size and growth patterns, and of colorimetric changes used as a proxy of physiological responses. Biochemical analyses revealed increased carotenoid and flavonoid content in chitosan + salicylic acid-treated plants with AMF, particularly in Principe Borghese. Genotype-dependent differences were evident in terms of fruit production, where Principe Borghese plants showed significantly more red fruits in presence of AM fungus. The results underline the potential of combined AMF and natural compound application as a sustainable strategy for improving tomato resilience to water stress, contributing to resource-efficient agricultural practices and climate change mitigation.

In the context of precision agriculture, high-throughput phenotyping (HTP) aims to rapidly and effectively identify factors that affect crop yield, enabling timely and appropriate interventions. However, interpreting data from HTP remains challenging. We performed a proximal red-green-blue (RGB)-based HTP on several tomato genotypes exposed to abiotic stress (drought) or biotic stress induced by tomato spotted wilt virus (TSWV), Pseudopyrenochaeta lycopersici (corky root rot; CRR), or Meloidogyne incognita (root-knot nematode; RKN). We aimed to determine if RGB-based HTP is effectively able to: a) distinguish the effects of biotic from abiotic stress; b) differentiate resistant/tolerant from susceptible genotypes. Our HTP data analysis produced 12 morphometric and eight colorimetric indices. Principal Component Analysis (PCA; P ​< ​0.0001; 83 ​% variation explained by three PCs) showed that factors such as shoot area solidity and certain color-based indices, including the senescence index and green area, effectively differentiated biotic from abiotic stress. Morphometric parameters, including plant height, projected shoot area, and convex hull area, proved to be applicable for identifying the stress status regardless of the type of stress. HTP effectively distinguished the genotype resistant to TSWV from the susceptible ones. This task was more challenging for below-ground stresses like CRR and RKN. Different profiles of HTP indices were observed among the genotypes assayed for drought tolerance, indicating variability in their ability to withstand drought conditions. In conclusion, our findings highlight the value of RGB-based HTP as a tool for precision farming of tomatoes, enabling the identification of both biotic and abiotic stressors.

A comprehensive and continuous analysis of relevant phenotypic traits in broadly diffused crops such as tomatoes is extremely important to assess the status of plants, especially in the current scenario of extreme climate events, which can threaten the foundations of the food supply chain for large populations. However, these operations are extremely costly in human effort; therefore, automating these processes is of paramount relevance. To this end, this work first provides a systematic benchmark of different, well-assessed versions of the You Only Look Once (YOLO) object detector, identifying the most suited baseline in terms of iteration and model density. Afterwards, the effectiveness of attention-based mechanisms was evaluated, highlighting possible critical aspects which may undermine overall results and real-time applicability. Thus, to enhance the baseline performance, an Incremental Learning pipeline was assessed, evaluating domain adaptation via fine-tuning. Specifically, a YOLOv11-based object detector was incrementally trained on specific subsets of a larger dataset, with the aim of providing the resulting model with subset-specific knowledge to allow more generalisation capabilities. This procedure resulted in an overall improvement of mAP@0.5 of about 1.36%, and F1 score of 1.1%, while slightly lowering the computational burden during inference, with an improvement of 19 ms. The effectiveness of these capabilities was also tested against a scenario with a low number of samples available, yielding promising domain adaptation results even under adverse conditions, and providing a practical path for evolving phenotypical evaluation under real-world agricultural scenarios.

Plant growth-promoting bacteria (PGPB) represents a sustainable strategy to improve the adaptability of plants to water shortage stress. The present study demonstrated that the endophyte Klebsiella pasteurii BDA134-6, isolated from O. glaberrima rice grown in Mali (West Africa), efficiently colonized the durum wheat variety Primadur. Results obtained from laboratory growth chamber, greenhouse phenotyping platform, and tunnel-type greenhouse demonstrated that inoculated Primadur plants improved their response to water shortage stress. When water shortage stress was simulated by treatment with polyethylene glycol, neither a significant decrease in the relative water content of leaves nor a strong inhibition of acetylene reduction was observed for inoculated plants compared to the uninoculated ones. Inoculated plants subjected to water shortage stress showed increased activity of the antioxidant enzymes glutathione reductase and superoxide dismutase (up to 38% and 191%, respectively), a proline content 3-fold higher, a reduced hydrogen peroxide accumulation (up to 50%), and no significant alterations in lipids peroxidation and leaf senescence. These findings highlight that BDA134-6 is a promising strain. Field trial experiments will allow to evaluate whether it can be used to improve antioxidant defense systems in a plant species different from the native host plant.

IntroductionKiwifruit species have a relatively high rate of root oxygen consumption, making them very vulnerable to low root zone oxygen concentrations resulting from soil waterlogging. Recently, kiwifruit rootstocks have been increasingly used to improve biotic and abiotic stress tolerance and crop performance under adverse conditions. The aim of the present study was to evaluate morpho-physiological changes in kiwifruit rootstocks and grafting combinations under short-term waterlogging stress.MethodsA pot trial was conducted at the ALSIA PhenoLab, part of the Phen-Italy infrastructures, using non-destructive RGB and NIR image-based analysis and physiological measurements to identify waterlogging stress indicators and more tolerant genotypes. Three pot-grown kiwifruit rootstocks (‘Bounty 71,’ Actinidia macrosperma—B; ‘D1,’ Actinidia chinensis var. deliciosa—D; and ‘Hayward,’ A. chinensis var. deliciosa—H) and grafting combinations, with a yellow-fleshed kiwifruit cultivar (‘Zesy 002,’ A. chinensis var. chinensis) grafted on each rootstock (Z/B, Z/D, Z/H), were subjected to a control irrigation treatment (WW), restoring their daily water consumption, and to a 9-day waterlogging stress (WL), based on substrate saturation. Leaf gas exchange, photosynthetic activity, leaf temperature, RGB, and NIR data were collected during waterlogging stress.ResultsStomatal conductance and transpiration reached very low values (less than 0.05 mol m−2 s−1 and 1 mmol m−2 s−1, respectively) in both waterlogged D and H rootstocks and their grafting combinations. In turn, leaf temperature was significantly increased and photosynthesis was reduced (1–6 μmol m−2 s−1) from the first days of waterlogging stress compared to B rootstock and combination.DiscussionThe B rootstock showed prolonged leaf gas exchange and photosynthetic activity, indicating that it can cope with short-term and temporary waterlogging and improve the tolerance of grafted kiwi vines, which showed a decrease in stomatal conductance 5 days after the onset of stress. Morphometric and colorimetric parameters from the image-based analysis confirmed the greater susceptibility of D and H rootstocks and their grafting combinations to waterlogging stress compared to B. The results presented confirm the role of physiological measurements and enhance that of RGB and NIR images in detecting the occurrence of water stress and identifying more tolerant genotypes in kiwifruit.

Rootstocks were the first sustainable and environmentally friendly strategy to cope with a major threat for Vitis vinifera cultivation. In addition to providing Phylloxera resistance, they play an important role in protecting against other soil-borne pests, such as nematodes, and in adapting V. vinifera to limiting abiotic conditions. Today viticulture has to adapt to ongoing climate change whilst simultaneously reducing its environmental impact. In this context, rootstocks are a central element in the development of agro-ecological practices that increase adaptive potential with low external inputs. Despite the apparent diversity of the Vitis genus, only few rootstock varieties are used worldwide and most of them have a very narrow genetic background. This means that there is considerable scope to breed new, improved rootstocks to adapt viticulture for the future. However, in comparison to the extensive research effort devoted to fruit varieties, there is little scientific knowledge to support grapevine rootstock breeding. Since grafting became widespread in viticulture, very few studies have been done on the genetic architecture of the relevant traits in rootstocks, even for resistance to Phylloxera or grafting ability. The current presentation will provide an overview of our knowledge on the genetics of specific rootstock traits, covering resistance to Phylloxera and nematodes, rooting and grafting abilities, and adaptation to drought and salinity. An attempt to list the resources and initiatives at the international level will be made. Acknowledgements: The research for rootstock breeding in Bordeaux has been supported over the years by numerous funding agencies and has benefited from the support of the wine industry. Louis Bordenave, Bernard Douens, Jean-Pierre Petit, Cyril Hévin and Nicolas Hocquard are to be acknowledged for their great involvement in the management of genetic resources and the monitoring of plant material.

Effective identification of tomato plant traits is crucial for timely monitoring and evaluating their growth and harvest. However, conducting stress experiments on multiple tomato genotypes introduces challenges due to the nature of the data. One of these challenges arises from an imbalanced sample distribution, potentially leading to misclassification between classes and disruptions in model recognition. This paper addresses the effect of these challenges by considering the imbalanced classes of flowers, fruits, and nodes and proposing an improved detection approach through data balancing. A novel data-balancing approach is introduced in this study to overcome the issue of imbalanced data. The proposed solution involves the implementation of a YOLOv8 deep learning model, which effectively detects flowers, fruits, and nodes in tomato plants. This model significantly enhances the ability of the algorithm to detect objects of varying sizes within complex environments. To further bolster the recognition capability of the targeted classes, the proposed model integrates a Squeeze-and-Excitation (SE) block attention module into its head architecture. This module strengthens the model recognition ability by giving increased attention to the studied classes, thereby enhancing overall detection performance. The results demonstrate that the data balancing approach successfully improves the model performance in response to the data challenges. When applying the technique of pre-training the optimal weights obtained from balanced data on imbalanced data, the SE-block module showed significant improvements in outcomes.

The paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were pictured and images segmented to extract the Red (R), Green (G), and Blue (B) mean pixel values which were re-arranged in 35 RGB-derived colorimetric indexes (CIs). After imaging, the samples were crushed and oil concentration was determined (NIR). The analysis of the correlation between oil and CIs revealed a differential hysteretic behavior depending on the covariates (CI and cultivar). The hysteresis area (Hyst) was then quantified and used to rank the CIs under the hypothesis that CIs with the maximum or minimum Hyst had the highest correlation coefficient and were the most suitable predictors within a general linear model. The results show that the predictors selected according to Hyst-based criteria had high accuracy as determined using a Global Performance Indicator (GPI) accounting for various performance metrics (R2, RSME, MAE). The use of a general linear model here presented is a new computational option integrating current methods mostly based on artificial neural networks. RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the transition of the olive sector towards a digital agriculture domain.

Grapevine is among the most economically important crops suffering environmental constraints, including drought and salt stress. Although imaging is increasingly used to detect abiotic stress in agriculture, image-based phenotyping in grapevine still needs optimisation. This study presents the RGB-(red, green, blue)-based phenotyping of the early stage of salt stress response in potted grapevine (Aleatico/SO4) irrigated with saline water (100 mM NaCl) for 9 days in contrast with vines irrigated with fresh water. The response was measured using stomatal conductance (gs), net photosynthetic rate (A), transpiration (E), maximum potential photosynthetic efficiency (Fv/Fm), stem water potential (SWP) concurrently with RGB imaging via a robotised platform. The image-based phenotyping of salt-stressed vines employed two sets of measurements: (i) the pixel fraction of specific colour bands (Yellow, Green, Brown and Dark Green) and (ii) the mean pixel value of R, G and B and other RGB-based colorimetric indexes. Results show that the responses of gs, A, E, Fv/Fm were closely related to increasing soil electrical conductivity (EC) and that imaging could detect the EC threshold of approx. 4 dS m-1 causing a ~60 % decrease in these physiological traits compared to the pre-stress level. The SWP declined to about –0.7 MPa at the end of the experiment. The change of the relative pixel fraction of Dark Green to increasing EC has been analysed within a dose-response context, showing that a decrease of 1 % of the Dark Green colour band corresponded to the 4 dS m-1 EC threshold. This study also examined the use of the mean pixel value of the R, G and B channels as proxies of EC along with new RGB-based indexes resulting from the rearrangement of original R, G and B mean pixel values. Results show the suitability of the mean pixel value of R and Coloration Index [(R-B)/R] to serve as predictors of EC (R2 >= 0.80).

Strawberry fruits, with their petite dimensions, possess an innate ability to blend into the surrounding foliage seamlessly. The size differences between the fruit and the background are such that they pose significant recognition challenges event to deep neural network models (DNN). With this in mind, this work is focused on exploring the latest model in the YOLO family, specifically YOLOv8, to address the complexities of a dataset gathered via a high-throughput phenotyping platform. As such, this study proposed a comprehensive analysis of optimized model head adaptations to enhance the detection of small objects starting from the base YOLOv8 architecture. This optimization is pursued while retaining the effectiveness of the model in identifying larger fruits and flowers. This optimization is poised to yield positive effects and contribute to more accurate and robust detection results on the proposed dataset.

Tomatoes are one of the most iconic crops in the Southern Mediterranean area, playing an important role both in social and economic terms, mainly due to large parts of the population strictly relying on these crops as the main food and income source. Consequently, variations in the yields of tomatoes could have large social and economic implications. However, extreme weather events, such as droughts, are increasingly common and widespread due to the ongoing climate crisis. Therefore, assessing the behaviour of crops in response to this type of stress is extremely important to guarantee a stable and reliable food source. To this end, this work proposes an end-to-end tomato evaluation strategy based on image processing and machine learning algorithms. The proposed pipeline allows domain experts to automatically evaluate relevant phenotypical traits of a population of genotypic mutants, comparing them with their Red Setter regarding the response to drought stress. The framework uses a simple, low-cost, and easy-to-use setup, which allows the rapid acquisition of a wide dataset. This can be acquired in different instants, providing temporal-dependent information on the response of the crops to different scenarios, which can be used to estimate relevant phenotypical traits. To evaluate its effectiveness, the processing pipeline was evaluated over two weeks, with a total of 388 mutants compared with 12 Red Setter, exploiting canopy coverage to assess the overall response of the mutants to drought stress.

To predict oil and phenol concentrations in olive fruit, the combination of back propagation neural networks (BPNNs) and contact-less plant phenotyping techniques was employed to retrieve RGB image-based digital proxies of oil and phenol concentrations. Fruits of cultivars (×3) differing in ripening time were sampled (~10-day interval, ×2 years), pictured and analyzed for phenol and oil concentrations. Prior to this, fruit samples were pictured and images were segmented to extract the red (R), green (G), and blue (B) mean pixel values that were rearranged in 35 RGB-based colorimetric indexes. Three BPNNs were designed using as input variables (a) the original 35 RGB indexes, (b) the scores of principal components after a principal component analysis (PCA) pre-processing of those indexes, and (c) a reduced number (28) of the RGB indexes achieved after a sparse PCA. The results show that the predictions reached the highest mean R2 values ranging from 0.87 to 0.95 (oil) and from 0.81 to 0.90 (phenols) across the BPNNs. In addition to the R2, other performance metrics were calculated (root mean squared error and mean absolute error) and combined into a general performance indicator (GPI). The resulting rank of the GPI suggests that a BPNN with a specific topology might be designed for cultivars grouped according to their ripening period. The present study documented that an RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the developing olive sector within a digital agriculture domain.

Recent developments in low-cost imaging hyperspectral cameras have opened up new possibilities for high-throughput phenotyping (HTP), allowing for high-resolution spectral data to be obtained in the visible and near-infrared spectral range. This study presents, for the first time, the integration of a low-cost hyperspectral camera Senop HSC-2 into an HTP platform to evaluate the drought stress resistance and physiological response of four tomato genotypes (770P, 990P, Red Setter and Torremaggiore) during two cycles of well-watered and deficit irrigation. Over 120 gigabytes of hyperspectral data were collected, and an innovative segmentation method able to reduce the hyperspectral dataset by 85.5% was developed and applied. A hyperspectral index (H-index) based on the red-edge slope was selected, and its ability to discriminate stress conditions was compared with three optical indices (OIs) obtained by the HTP platform. The analysis of variance (ANOVA) applied to the OIs and H-index revealed the better capacity of the H-index to describe the dynamic of drought stress trend compared to OIs, especially in the first stress and recovery phases. Selected OIs were instead capable of describing structural changes during plant growth. Finally, the OIs and H-index results have revealed a higher susceptibility to drought stress in 770P and 990P than Red Setter and Torremaggiore genotypes.

In the scenario of climate change, the availability of genetic resources for tomato cultivation that combine improved nutritional properties and more tolerance to water deficiency is highly desirable. Within this context, the molecular screenings of the Red Setter cultivar-based TILLING platform led to the isolation of a novel lycopene ε-cyclase gene (SlLCY-E) variant (G/3378/T) that produces modifications in the carotenoid content of tomato leaves and fruits. In leaf tissue, the novel G/3378/T SlLCY-E allele enhances β,β-xanthophyll content at the expense of lutein, which decreases, while in ripe tomato fruit the TILLING mutation induces a significant increase in lycopene and total carotenoid content. Under drought stress conditions, the G/3378/T SlLCY-E plants produce more abscisic acid (ABA) and still conserve their leaf carotenoid profile (reduction of lutein and increase in β,β-xanthophyll content). Furthermore, under said conditions, the mutant plants grow much better and are more tolerant to drought stress, as revealed by digital-based image analysis and in vivo monitoring of the OECT (Organic Electrochemical Transistor) sensor. Altogether, our data indicate that the novel TILLING SlLCY-E allelic variant is a valuable genetic resource that can be used for developing new tomato varieties, improved in drought stress tolerance and enriched in fruit lycopene and carotenoid content.

Durum wheat is a rain-fed crop mainly cultivated in the Mediterranean basin and threatened by climate change. In particular, drought stress is one of the major constraints that can negatively affect crops production worldwide. In the present study we characterized drought stress responses in a set of durum wheat genotypes by combining plant growth parameters analysed using a plant phenotyping platform, with physiological parameters derived from gas-exchange measurements, namely exchanges of CO 2 , water vapor, and profile of emitted Volatile Organic Compounds (VOCs), in order to develop new programs for precision agriculture.

Imaging is an emerging contact-less high-throughput technology employed to retrieve quantitative and qualitative plant traits. In addition, it is often combined with artificial neural networks (ANNs) to further improve the reliability of image-based digital proxies. The olive oil industry is expanding globally as olive oil is increasingly recognized as a functional food. Fast and reliable determination of fruit quality traits is challenging in the agricultural sector. This study summarizes recent advances in the use of RGB-based imaging combined with ANNs to (i) predict oil and phenol concentrations in olive fruit and (ii) classify fruit at harvest according to colour and defects. Opportunities and limitations are also discussed.