The Decrease of Leaf Dark Respiration during Water Stress Is Related to Leaf Non-Structural Carbohydrate Pool in Vitis vinifera L.

Dark respiration (Rd) is a fundamental plant process used to gain biomass and maintain plant physiological activity. It accounts for the metabolization of a large share of the carbon fixed by photosynthesis. However, Rd during conditions of severe plant water stress is still poorly understood. The decrease in leaf transpiration increases temperature, one of the most important drivers of leaf Rd. On the other hand, water stress decreases the pool of leaf carbohydrates, which are the most important substrate for respiration. The aim of the present work was to determine the impact of water shortage on leaf Rd in grapevine and understand the driving factors in modulating leaf Rd response under plant water stress conditions. Water stressed vines had lower Rd as the water shortage severity increased. Rd was correlated with leaf temperature in well-watered vines. Instead, in water stressed vines, Rd correlated with leaf soluble sugars. The decrease of leaf Rd in water stressed vines was due to the decrease of leaf non-structural carbohydrate that, under water stress conditions, exerted a limiting effect on Rd.

HortNet417v1—A Deep-Learning Architecture for the Automatic Detection of Pot-Cultivated Peach Plant Water Stress

The biggest challenge in the classification of plant water stress conditions is the similar appearance of different stress conditions. We introduce HortNet417v1 with 417 layers for rapid recognition, classification, and visualization of plant stress conditions, such as no stress, low stress, middle stress, high stress, and very high stress, in real time with higher accuracy and a lower computing condition. We evaluated the classification performance by training more than 50,632 augmented images and found that HortNet417v1 has 90.77% training, 90.52% cross validation, and 93.00% test accuracy without any overfitting issue, while other networks like Xception, ShuffleNet, and MobileNetv2 have an overfitting issue, although they achieved 100% training accuracy. This research will motivate and encourage the further use of deep learning techniques to automatically detect and classify plant stress conditions and provide farmers with the necessary information to manage irrigation practices in a timely manner.

Physiological and Transcriptomic Analyses Revealed the Implications of Abscisic Acid in Mediating the Rate-Limiting Step for Photosynthetic Carbon Dioxide Utilisation in Response to Vapour Pressure Deficit in Solanum Lycopersicum (Tomato)

The atmospheric vapour pressure deficit (VPD) has been demonstrated to be a significant environmental factor inducing plant water stress and affecting plant photosynthetic productivity. Despite this, the rate-limiting step for photosynthesis under varying VPD is still unclear. In the present study, tomato plants were cultivated under two contrasting VPD levels: high VPD (3–5 kPa) and low VPD (0.5–1.5 kPa). The effect of long-term acclimation on the short-term rapid VPD response was examined across VPD ranging from 0.5 to 4.5 kPa. Quantitative photosynthetic limitation analysis across the VPD range was performed by combining gas exchange and chlorophyll fluorescence. The potential role of abscisic acid (ABA) in mediating photosynthetic carbon dioxide (CO2) uptake across a series of VPD was evaluated by physiological and transcriptomic analyses. The rate-limiting step for photosynthetic CO2 utilisation varied with VPD elevation in tomato plants. Under low VPD conditions, stomatal and mesophyll conductance was sufficiently high for CO2 transport. With VPD elevation, plant water stress was gradually pronounced and triggered rapid ABA biosynthesis. The contribution of stomatal and mesophyll limitation to photosynthesis gradually increased with an increase in the VPD. Consequently, the low CO2 availability inside chloroplasts substantially constrained photosynthesis under high VPD conditions. The foliar ABA content was negatively correlated with stomatal and mesophyll conductance for CO2 diffusion. Transcriptomic and physiological analyses revealed that ABA was potentially involved in mediating water transport and photosynthetic CO2 uptake in response to VPD variation. The present study provided new insights into the underlying mechanism of photosynthetic depression under high VPD stress.

Nitrogen Status and Canopy Density Detection of Vegetation Using Multispectral Data

The use of ground-based multispectral data for the evaluation of plant water stress and nitrogen status in Old Oyo national Park, Nigeria was conducted and classification was performed. The active area of nitrogen concentration in hectares was between 0.0 and 0.4. This active area was expressed in hectares (ha) and percentages (%) respectively. From the assessment, 53.52 ha (69%) and 24.29 ha (31%) were recorded at 0.2-0.4 and 0.0-0.2 ha, respectively. Heat stress takes place when the regular temperature is above 30 °C, which could slow down plant growth and lead to the threat of deficiency. The heat stress reached a maximum of 40 °C in this analysis between February and May. The research concluded that the sustainability of crops and trees requires a certain quantity of 69 percent nitrogen and a certain level of wetness for their growth which is between 400 mm and 800 mm rainfall.

Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard

Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.

Partial energy balance closure of eddy covariance evaporation measurements using concurrent lysimeter observations over grassland

With respect to ongoing discussions about the causes of energy imbalance and approaches to force energy balance closure, a method has been proposed that allows partial latent heat flux closure (Widmoser and Wohlfahrt, 2018). In the present paper, this method is applied to four measurement stations over grassland under humid and semiarid climates, where lysimeter (LY) and eddy covariance (EC) measurements were taken simultaneously. The results differ significantly from the ones reported in the literature. We distinguish between the resulting EC values being weakly and strongly correlated to LY observations as well as systematic and random deviations between the LY and EC values. Overall, an excellent match could be achieved between the LY and EC measurements after applying evaporation-linked weights. But there remain large differences between the standard deviations of the LY and adjusted EC values. For further studies we recommend data collected at time intervals even below 0.5 h. No correlation could be found between evaporation weights and weather indices. Only for some datasets, a positive correlation between evaporation and the evaporation weight could be found. This effect appears pronounced for cases with high radiation and plant water stress. Without further knowledge of the causes of energy imbalance one might perform full closure using equally distributed weights. Full closure, however, is not dealt with in this paper.

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.