Image-Based Assessment of Drought Response in Grapevines

Comparative transcriptome and metabolome profiles of the leaf and fruits of a Xianjinfeng litchi budding mutant and its mother plant

Background: Litchi (Litchi chinensis) is an important sub-tropical fruit in the horticulture market in China. Breeding for improved fruit characteristics is needed for satisfying consumer demands. Budding is a sustainable method for its propagation. During our ongoing breeding program, we observed a litchi mutant with flat leaves and sharp fruit peel cracking in comparison to the curled leaves and blunt fruit peel cracking fruits of the mother plant. Methods: To understand the possible molecular pathways involved, we performed a combined metabolome and transcriptome analysis. Results: We identified 1,060 metabolites in litchi leaves and fruits, of which 106 and 101 were differentially accumulated between the leaves and fruits, respectively. The mutant leaves were richer in carbohydrates, nucleotides, and phenolic acids, while the mother plant was rich in most of the amino acids and derivatives, flavonoids, lipids and organic acids and derivatives, and vitamins. Contrastingly, mutant fruits had higher levels of amino acids and derivatives, carbohydrates and derivatives, and organic acids and derivatives. However, the mother plant's fruits contained higher levels of flavonoids, scopoletin, amines, some amino acids and derivatives, benzamidine, carbohydrates and derivatives, and some organic acids and derivatives. The number of differentially expressed genes was consistent with the metabolome profiles. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enriched gene expressions showed consistent profiles as of metabolome analysis. Conclusion: These results provide the groundwork for breeding litchi for fruit and leaf traits that are useful for its taste and yield.

Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees

Field-grown peach trees are large and have a complex branch structure; therefore, detection of water deficit stress from images is challenging. We obtained large datasets of images of field-grown peach trees with continuous values of stem water potential (Ψstem) through partial secession treatment of the base of branches to change the water status of the branches. The total number of images as frames extracted from videos of branches was 23,181, 6743, and 10,752, in the training, validation, and test datasets, respectively. These datasets enabled us to precisely model water deficit stress using a deep-learning-regression model. The predicted Ψstem of frames belonging to a single branch showed a Gaussian distribution, and the coefficient of determination between the measured and predicted values of Ψstem increased to 0.927 by averaging the predicted values of the frames in each video. This method of averaging the predicted values of frames in each video can automatically eliminate noise and summarize data into the representative value of a tree and is considered to be robust for the diagnosis of water deficit stress in large field-grown peach trees with a complex branch structure.

Comparative effects of drought stress on leaf gas exchange, foliar ABA and leaf orientation in four grapevine cultivars grown in Northern Italy

Drought tolerance varies greatly across Vitis vinifera cultivars, depending on physiological responses and structural and morphological adaptations. In this study, responses to water stress were examined in three extensively cultivated varieties from Northern Italy. Over the course of two seasons, mature potted vines were subjected to a 12 or 13-day period of water restriction. Vine water relations were investigated using measures of water potential, gas exchanges, and leaf ABA content. Leaf angle response to increasing water stress was analysed in the four cultivars as a mechanism that improves stress tolerance. Different physiological responses were observed among cultivars, suggesting a near-isohydric water-use strategy for Moscato and a near-anisohydric one for Garganega, Glera and Merlot. Results of leaf ABA analysis highlighted a variability among the studied varieties, indicating higher contents and lower sensitivity to ABA for the anisohydric ones. In all varieties, a similar increase in midday leaf inclination was observed in response to decreasing stem water potentials, indicating that leaf angle adjustments may represent a common adaptive response to drought. These findings increase the understanding of the leaf physiological and structural mechanisms that contribute to water stress tolerance in grapevine, supporting a more efficient cultivar selection to cope with the expected changes in Mediterranean climate.

An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review

Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.

Investigating high throughput phenotyping based morpho-physiological and biochemical adaptations of indian pennywort (Centella asiatica L. urban) in response to different irrigation regimes

Indian pennywort (Centella asiatica L. Urban; Apiaceae) is a herbaceous plant used as traditional medicine in several regions worldwide. An adequate supply of fresh water in accordance with crop requirements is an important tool for maintaining the productivity and quality of medicinal plants. The objective of this study was to find a suitable irrigation schedule for improving the morphological and physiological characteristics, and crop productivity of Indian pennywort using high-throughput phenotyping. Four treatments were considered based on irrigation schedules (100, 75, 50, and 25% of field capacity denoted by I100 [control], I75, I50, and I25, respectively). The number of leaves, plant perimeter, plant volume, and shoot dry weight were sustained in I75 irrigated plants, whereas adverse effects on plant growth parameters were observed when plants were subjected to I25 irrigation for 21 days. Leaf temperature (Tleaf) was also retained in I75 irrigated plants, when compared with control. An increase of 2.0 °C temperature was detected in the Tleaf of plants under I25 irrigation treatment when compared with control. The increase in Tleaf was attributed to a decreased transpiration rate (R2 = 0.93), leading to an elevated crop water stress index. Green reflectance and leaf greenness remained unchanged in plants under I75 irrigation, while significantly decreased under I50 and I25 irrigation. These decreases were attributed to declined leaf osmotic potential, increased non-photochemical quenching, and inhibition of net photosynthetic rate (Pn). The asiatic acid and total centellosides in the leaf tissues, and centellosides yield of plants under I75 irrigation were retained when compared with control, while these parameters were regulated to maximal when exposed to I50 irrigation. Based on the results, I75 irrigation treatment was identified as the optimum irrigation schedule for Indian pennywort in terms of sustained biomass and a stable total centellosides. However, further validation in the field trials at multiple locations and involving different crop rotations is recommended to confirm these findings.

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

Wind speed, sun exposure and water status alter sunburn susceptibility of grape berries

In the context of climate change, yield and quality losses from sunburn necrosis are challenging grape growers around the world. In a previous review, we identified the role of wind speed, duration of heat exposure, drought stress and adaptation as major knowledge gaps that prevent a better predictability of sunburn events. In this paper we present results of targeted experiments aiming to close these knowledge gaps. The effects of drought stress and adaptation on sunburn susceptibility were investigated in a combined drought stress/ defoliation experiment. Riesling grapevines growing in an arid climate were fully irrigated or drought stressed, and clusters were exposed to sunlight by fruit-zone leaf removal (defoliation) at two developmental stages. Sunburn symptoms were induced using infrared heaters while fruit surface temperature was measured using thermal imaging enabling the establishment of threshold temperatures. The influence of the duration of heat exposure of berries was examined by heating grape clusters to a stable temperature and monitoring the evolution of sunburn symptoms over time. To examine the effects of wind speed on the appearance of sunburn necrosis symptoms, fruit surface temperatures and sunburn severity were measured along an artificially induced wind speed gradient in two cultivars using thermal imaging and visual inspection. Longer durations of heat exposure required lower fruit surface temperatures to induce damage, while the differences in temperature after 60 min and 90 min of exposure were marginal (47.82 ± 0.25 °C and 47.06 ± 0.26 °C). Clusters of vines grown under water deficit were less susceptible to sunburn compared to those of well-irrigated plants following defoliation. The lethal temperature of clusters exposed to sunlight for seven days did not differ from those exposed to sunlight for 28 days, indicating that a full adaptation ocurred within this period. Higher wind speeds led to lower cluster temperatures and reduced sunburn severity. First evidence of a drought priming induced heat tolerance of grapevine berries was found, while adaptation had a more pronounced effect on the susceptibility to sunburn compared to water stress.

Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance

Climate change amplifies the intensity and occurrence of dry periods leading to drought stress in vegetation. For monitoring vegetation stresses, sun-induced chlorophyll fluorescence (SIF) observations are a potential game-changer, as the SIF emission is mechanistically coupled to photosynthetic activity. Yet, the benefit of SIF for drought stress monitoring is not yet understood. This paper analyses the impact of drought stress on canopy-scale SIF emission and surface reflectance over a lettuce and mustard stand with continuous field spectrometer measurements. Here, the SIF measurements are linked to the plant’s photosynthetic efficiency, whereas the surface reflectance can be used to monitor the canopy structure. The mustard canopy showed a reduction in the biochemical component of its SIF emission (the fluorescence emission efficiency at 760 nm—ϵ760) as a reaction to drought stress, whereas its structural component (the Fluorescence Correction Vegetation Index—FCVI) barely showed a reaction. The lettuce canopy showed both an increase in the variability of its surface reflectance at a sub-daily scale and a decrease in ϵ760 during a drought stress event. These reactions occurred simultaneously, suggesting that sun-induced chlorophyll fluorescence and reflectance-based indices sensitive to the canopy structure provide complementary information. The intensity of these reactions depend on both the soil water availability and the atmospheric water demand. This paper highlights the potential for SIF from the upcoming FLuorescence EXplorer (FLEX) satellite to provide a unique insight on the plant’s water status. At the same time, data on the canopy reflectance with a sub-daily temporal resolution are a promising additional stress indicator for certain species.

Photoprotection Is Achieved by Photorespiration and Modification of the Leaf Incident Light, and Their Extent Is Modulated by the Stomatal Sensitivity to Water Deficit in Grapevines

Absorbed energy in excess of that used by photosynthesis induces photoinhibition, which is common in water deficit conditions, resulting in reductions in stomatal conductance. In grapevines, controlled water deficit is a common field practice, but little is known about the impact of a given water shortage on the energy transduction processes at the leaf level in relation to contrasting stomatal sensitivities to drought. Here, we assessed the effect of a nearly similar water deficit condition on four grapevine varieties: Cabernet Sauvignon (CS) and Sauvignon Blanc (SB), which are stomatal sensitive, and Chardonnay (CH) and Carménère (CM), which are less stomatal sensitive, grown in 20 L pots outdoors. Plants were maintained to nearly 94% of field capacity (WW) and 83% field capacity (WD). We have assessed plant water status, photosynthesis (A_N), photorespiration, A_N vs. PAR, AC_i curves, photochemical (qP) and non-photochemical (qN) fluorescence quenching vs. PAR, the photoprotective effectiveness of NPQ (qPd) and light interception by leaves. Photorespiration is important under WD, but to a different extent between varieties. This is related to stomatal sensitivity, maintaining a safe proportion of PSII reaction centres in an open state. Additionally, the capacity for carboxylation is affected by WD, but to a greater extent in more sensitive varieties. As for qN, in WD it saturates at 750 μmol PAR m⁻²s⁻¹, irrespective of the variety, which coincides with PAR, from which qN photoprotective effectiveness declines, and qP is reduced to risky thresholds. Additionally, that same PAR intensity is intercepted by WD leaves from highly stomatal-sensitive varieties, likely due to a modification of the leaf angle in those plants. Pigments associated with qN, as well as chlorophylls, do not seem to be a relevant physiological target for acclimation.

Innovation in grapevine water status monitoring and drought adaptation: leaf angle and temperature regulation

Increase of frequency, duration, and intensity of drought and heatwave and related water and heat crops stress are among the principal effects of climate change. This paper reports: (i) the effect of calcite particle film (CaPF) as a mitigation strategy against heat stress in well-watered (WW) or in drought-stress (D) conditions; and (ii) response of leaf angle variation to stomatal conductance changes induced by drought stress of Aleatico grapevine cultivar. Results have showed that CaPF, under WW conditions, reduced leaf temperature, and increased gas exchange, but, under very severe water stress, CaPF treatment was ineffective. Leaf angle ranged from 70° (WW vines) to 100° (drought stressed vines) and showed a good fit (R2 =0.81) with stomatal conductance within the range of 0.25 – 0.05 mol m-2 s–1 proving it might be a reliable proxy of vine water status.

New approaches to improve crop tolerance to biotic and abiotic stresses

During the last years, a great effort has been dedicated at the development and employment of diverse approaches for achieving more stress-tolerant and climate-flexible crops and sustainable yield increases to meet the food and energy demands of the future. The ongoing climate change is in fact leading to more frequent extreme events with a negative impact on food production, such as increased temperatures, drought, and soil salinization as well as invasive arthropod pests and diseases. In this review, diverse "green strategies" (e.g., chemical priming, root-associated microorganisms), and advanced technologies (e.g., genome editing, high-throughput phenotyping) are described on the basis of the most recent research evidence. Particularly, attention has been focused on the potential use in a context of sustainable and climate-smart agriculture (the so called "next agriculture generation") to improve plant tolerance and resilience to abiotic and biotic stresses. In addition, the gap between the results obtained in controlled experiments and those from application of these technologies in real field conditions (lab to field step) is also discussed.

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36 ± 0.53° for pitch and 0.50 ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e. bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.

Missing Links in Predicting Berry Sunburn in Future Vineyards

Sunburn in grapevine berries is known as a recurring disorder causing severe yield losses and a decline in berry quality. The transition from healthy to sunburnt along a temporal trajectory is not fully understood. It is driven by light-boosted local heat impact and modulated by, e.g., past environments of the berry and its developmental state. Events of berry sunburn are often associated with heatwaves, indicating a link to climate change. In addition, the sensitivity of grapevine architecture to changing environmental condition indicates an urgent need to investigate and adapt mitigation strategies of berry sunburn in future vineyards. In this perspective, we want to identify missing links in predicting berry sunburn in vineyards and propose a modeling framework that may help us to investigate berry sunburn in future vineyards. For this, we propose to address open issues in both developing a model of berry sunburn and considering dynamic canopy growth, and canopy interaction with the environment and plant management such as shoot positioning or leaf removal. Because local environmental conditions drive sunburn, we aim at showing that identifying sunburn-reducing strategies in a vineyard under future environmental conditions can be supported by a modeling approach that integrates effects of management practices over time and takes grapevine architecture explicitly into account. We argue that functional-structural plant models may address such complex tasks. Once open issues are solved, they might be a promising tool to advance our knowledge on reducing risks of berry sunburn in silico.

Deep Segmentation of Point Clouds of Wheat

The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to analyse their traits directly in 3D space. Point clouds have no regular grid and thus their segmentation is challenging. Pattern-Net creates a dynamic link among neighbors to seek stable patterns from a 3D point set across several levels of abstraction using the K-nearest neighbor algorithm. To this end, different layers are connected to each other to create complex patterns from the simple ones, strengthen dynamic link propagation, alleviate the vanishing-gradient problem, encourage link reuse and substantially reduce the number of parameters. The proposed deep network is capable of analysing and decomposing unstructured complex point clouds into semantically meaningful parts. Experiments on a wheat dataset verify the effectiveness of our approach for segmentation of wheat in 3D space.