Projected Leaf Area Research Articles

The threat from ozone to vegetation in Ireland

Ozone is the most damaging air pollutant to vegetation globally. Metrics of accumulated ozone above a concentration threshold (e.g. AOT40, ppb·h) have been widely used to assess ozone risk. However, there is growing consensus that accumulated Phytotoxic Ozone Dose (POD) above a receptor-specific critical stomatal flux threshold (y; nmol O3 m−2 s−1), expressed per unit of projected leaf area, provides a more reliable risk assessment, as it considers ozone entering the leaf (PODy, mmol m−2 leaf area). Few studies have assessed both concentration- and flux-based metrics using site-specific observations of ozone and meteorology. In this study we assessed the risk that ozone poses to five vegetation types across eight sites in Ireland during 2005–2021, using AOT40 and PODy risk metrics, and we predicted impacts using dose–response relationships. Long-term trends in both metrics were also assessed. The PODy critical level for vegetation protection was exceeded for all vegetation types, with exceedances most common at Atlantic coastal sites, and for tree species (beech POD1 15.7–25.7 mmol/m2 PLA). When PODy and AOT40 results were normalised based on their respective critical levels, predicted impacts were higher for PODy. There were significant increases in PODy for three vegetation types at rural sites during the study period, which also experienced increases in temperature and global solar radiation. The long-term trends were consistent with other European studies that show decreases in AOT40 and increases in PODy. While ozone concentrations in Ireland are relatively low (39–75 μg/m3 five-year average range), the humid climate and longer growing season may lead to elevated stomatal ozone uptake and thereby a risk to vegetation.

Unraveling the difference of sensitivity to ozone between non-hybrid native poplar and hybrid poplar clones: A flux-based dose-response analysis

Poplars are economically important tree crops and biologically important model plants, which are known to be sensitive to ozone (O3). Although surface O3 is considered as a significant global environmental issue because of its phytotoxicity and greenhouse effect, the knowledge of the dose-response (DR) relationships in poplars for the assessment of O3 risk is still limited. Hence, this study aimed at collecting data of studies with manipulative O3 exposures of poplars within FACE (Free Air Concentration Enhancement) and OTC (Open-Top Chamber) facilities. The datasets contain studies on hybrid poplar clones and a non-hybrid native poplar (Populus nigra L.) reporting both AOT40 (Accumulated exposure Over a Threshold of 40 ppb) and POD1 (Phytotoxic Ozone Dose above a threshold of 1 nmol m−2 Projected Leaf Area [PLA] s−1) to compare exposure- and flux-based indices. As a result, linear regression analysis showed that the flux-based POD1 was better than the exposure-based AOT40 to explain the biomass response of poplars to O3. From the DR relationships, a critical level (CL) of 5.7 mmol m−2 POD1 has been derived corresponding to 4% biomass growth reduction for hybrid poplar clones, which can be considered very sensitive to O3, while the non-hybrid native poplar was less sensitive to O3 (CL: 10.3 mmol m−2 POD1), although the potential risk of O3 for this taxon is still high due to very high stomatal conductance. Moreover, the different experimental settings (OTC vs. FACE) have affected the AOT40-based DR relationships but not the POD1-based DR relationships, suggesting that poplar responses to O3 were principally explained by stomatal O3 uptake regardless of the different experimental settings and exposure patterns. These results highlight the importance of the flux-based approach, especially when scaling up from experimental datasets to the O3 risk assessment for poplars at the regional or global scale.

Automated Phenotypic Trait Extraction for Rice Plant Using Terrestrial Laser Scanning Data.

To quickly obtain rice plant phenotypic traits, this study put forward the computational process of six rice phenotype features (e.g., crown diameter, perimeter of stem, plant height, surface area, volume, and projected leaf area) using terrestrial laser scanning (TLS) data, and proposed the extraction method for the tiller number of rice plants. Specifically, for the first time, we designed and developed an automated phenotype extraction tool for rice plants with a three-layer architecture based on the PyQt5 framework and Open3D library. The results show that the linear coefficients of determination (R2) between the measured values and the extracted values marked a better reliability among the selected four verification features. The root mean square error (RMSE) of crown diameter, perimeter of stem, and plant height is stable at the centimeter level, and that of the tiller number is as low as 1.63. The relative root mean squared error (RRMSE) of crown diameter, plant height, and tiller number stays within 10%, and that of perimeter of stem is 18.29%. In addition, the user-friendly automatic extraction tool can efficiently extract the phenotypic features of rice plant, and provide a convenient tool for quickly gaining phenotypic trait features of rice plant point clouds. However, the comparison and verification of phenotype feature extraction results supported by more rice plant sample data, as well as the improvement of accuracy algorithms, remain as the focus of our future research. The study can offer a reference for crop phenotype extraction using 3D point clouds.

Optimizing photosynthetic photon flux density and light quality for maximizing space use efficacy in edamame at the vegetative growth stage

Compared with conventional crop cultivation in greenhouses or fields, plant factories with artificial light (PFAL) have advantages in the highly efficient use of space, energy, and resources available for cultivation. However, few studies on environmental controls for improving the space use efficacy (SUE) of PFAL in the production of edamame, a vegetable soybean, have been reported. Therefore, developing an environmental control method for high productivity with minimal space and energy requirements is of high priority. The aims of this study were to (1) identify the optimal photosynthetic photon flux density (PPFD) and light quality to enhance the SUE of edamame at the vegetative growth stage, and (2) examine the effects of PPFD, light quality, and their interaction on edamame plant growth at the vegetative stage. SUE is defined as the crop biomass produced per unit cubic volume of cultivation during the growth period. We examined three PPFD treatments (300, 500, and 700 μmol m−2 s−1) with three color temperature LED lamps (3,000, 5,000, and 6,500 K), for a total of nine treatments. The results demonstrated that, under the same light quality treatment, higher PPFDs resulted in larger fresh and dry weights of all organs, higher stem length, and lower specific leaf area. Under the same PPFD treatment, a high ratio of blue (400–499 nm) to red (600–699 nm) photon flux density increased the plant height but decreased the projected leaf area. The values of SUE at 700 μmol m−2 s−1 increased by 213, 163, and 92% with 3,000, 5,000, and 6,500 K, respectively compared with those at 300 μmol m−2 s−1. The values of SUE at 700 μmol m−2 s−1 increased by 34 and 23% in 5,000 and 6,500 K treatments, respectively compared with that in the 3,000 K treatment. In conclusion, a combination of 700 μmol m−2 s−1 PPFD and 5,000 K color temperature is the suitable condition to increase the SUE of edamame at the vegetative growth stage in a PFAL.

Effect of Shading in an Agri-PV System on Structure and Growth of Ornamental Plants

The impact of shading on selected ornamental plant species was investigated by monitoring plant growth under a nontransparent roof in a nursery in Jülich and in an AgriPV-System in Rathenow. Plants were continuously measured using different RGB camera systems. Shading led to an increase in projected leaf area, increased petiole length and specific leaf area. Morphological changes in shade-sensitive Geranium cinereum plants led to a loss of plant marketability. Flowering time of Hydrangea sp. was not affected in a long term experiment in the AgriPV-System. Pigment composition was not altered significantly in Rhododendron plants. Experiments will be continued with a local nursery in a novel AgriPV-System established near Jülich.

Estimation of plant's morphological parameters using terrestrial laser scanning-based three-dimensional point cloud data

The yield of crop is very important for sustainable agriculture to meet continuously growing demand of food. The morphological parameters, such as plant height (PH), projected leaf area (PLA), and plant volume (PV) are important metrics to estimate crop yield. In this paper, a multi-step methodology is proposed to estimate parameters PH, PLA and PLA using state-of-the-art three-dimensional (3D) mapping technique: terrestrial laser scanning (TLS). The proposed methodology consists of three well-designed steps: pre-processing, ground filtering and clustering, and estimation of plant's morphological parameters. In pre-processing TLS-derived laser scans acquired from multiple scan stations to cover the selected agricultural test site registered and coordinate transformation is performed to convert the TLS point cloud into total station (TS)-based locally defined coordinate system. The second step transformed point cloud data is processed to generate ground and non-ground points, where subsequently Euclidean clustering is employed to generate clusters of non-ground points. The clustered data is cleaned based on geometrical constraints and cabbage plant's clusters are extracted, which are processed using height-based analysis and convex hull algorithm to estimate PH, PLA and PLA of each cluster. The proposed methodology performance was evaluated using TLS point cloud data of selected agricultural field of cabbage plants, and PH, PLA and PLA estimation was performed at RMSE of 0.005 m, 0.012 m2 and 0.0063 m3 respectively. The proposed methodology is efficient and easy to implement step-wise approach from data collection to processing for plant's geometrical analysis, thus it has potential to be used as one of the industrial tools for precision agriculture.

Pitfalls and potential of high-throughput plant phenotyping platforms.

Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.

From urine to food and oxygen: effects of high and low NH4+:NO3- ratio on lettuce cultivated in a gas-tight hydroponic facility.

In situ production of food, water and oxygen is essential for long-duration human space missions. Higher plants represent a key element in Bioregenerative Life Support Systems (BLSS), where crop cultivation can be based on water and nutrients recovered from waste and wastewater. Human urine exemplifies an important waste stream with potential to provide crops with nitrogen (N) and other nutrients. Dynamic waste composition and treatment processes may result in mineralized fractions with varying ammonium (NH4 +) to nitrate (NO3 -) ratios. In this study, lettuce was cultivated in the unique ESA MELiSSA Plant Characterization Unit, an advanced, gas-tight hydroponic research facility offering controlled environment and continuous monitoring of atmospheric gas composition. To evaluate biological and system effects of nutrient solution NH4 +:NO3 - ratio, two crop tests were run with different NH4 + to total N ratio (NH4 +:N) and elevated concentrations of Na+ and Cl- in line with a urine recycling scenario. Plants cultivated at 0.5 mol·mol-1 NH4 +:N (HiNH4 +) achieved 50% lower shoot biomass compared to those cultivated at 0.1 mol·mol-1 NH4 +:N (LoNH4 +), accompanied by higher shoot dry weight content and lower harvest index. Analyses of projected leaf area over time indicated that the reduced biomass observed at harvest could be attributed to a lower specific growth rate during the close-to-exponential growth phase. The HiNH4 + crop produced 40% less O2 over the full cultivation period. However, normalization of the results indicated a marginal increase in O2 production per time and per projected leaf area for the HiNH4 + crop during the exponential growth phase, in line with a higher shoot chlorophyll content. Mineral analysis demonstrated that the biomass content of NH4 + and NO3 - varied in line with the nutrient solution composition. The ratio of consumed NH4 + to consumed N was higher than the NH4 +:N ratio of the nutrient solution for both crop tests, resulting in decreasing NH4 +:N ratios in the nutrient solution over time. The results provide enhanced insight for design of waste processes and crop cultivation to optimize overall BLSS efficiency and hold valuable potential for improved resource utilization also in terrestrial food production systems.

Plant size directly correlates with water use efficiency in Arabidopsis.

Plant transpiration is a fundamental process that determines plant water use efficiency (WUE), thermoregulation, nutrition, and growth. How transpiration impacts on such essential physiological aspects and how the environment modulates these effects are fundamental questions about which little is known. We investigated the genetic and environmental factors underlying natural variation in plant transpiration and water use efficiency in a population of natural Arabidopsis thaliana accessions grown under homogeneous conditions. As expected, we observed large variation of total transpiration capacity, transpiration per surface unit, and WUE among A. thaliana accessions. Despite the variation of stomatal density and ABA content in the population, WUE did not correlate with any of these parameters. On the contrary, a surprising direct correlation was found between WUE and projected leaf area, with bigger plants displaying a more efficient use of water. Importantly, genome-wide association studies further supported our observations through the identification of several loci involved in WUE variation, mutations in which caused a simultaneous reduction in plant size and a decrease in WUE. Altogether, our results strongly suggest that, although WUE depends on many parameters, plant size is an adaptive trait with respect to water use in A. thaliana.

Dynamic growth QTL action in diverse light environments: characterization of light regime-specific and stable QTL in Arabidopsis.

Plant growth is a complex process affected by a multitude of genetic and environmental factors and their interactions. To identify genetic factors influencing plant performance under different environmental conditions, vegetative growth was assessed in Arabidopsis thaliana cultivated under constant or fluctuating light intensities, using high-throughput phenotyping and genome-wide association studies. Daily automated non-invasive phenotyping of a collection of 382 Arabidopsis accessions provided growth data during developmental progression under different light regimes at high temporal resolution. Quantitative trait loci (QTL) for projected leaf area, relative growth rate, and PSII operating efficiency detected under the two light regimes were predominantly condition-specific and displayed distinct temporal activity patterns, with active phases ranging from 2 d to 9 d. Eighteen protein-coding genes and one miRNA gene were identified as potential candidate genes at 10 QTL regions consistently found under both light regimes. Expression patterns of three candidate genes affecting projected leaf area were analysed in time-series experiments in accessions with contrasting vegetative leaf growth. These observations highlight the importance of considering both environmental and temporal patterns of QTL/allele actions and emphasize the need for detailed time-resolved analyses under diverse well-defined environmental conditions to effectively unravel the complex and stage-specific contributions of genes affecting plant growth processes.

A regional scale flux-based O3 risk assessment for winter wheat in northern Italy, and effects of different spatio-temporal resolutions

Tropospheric ozone (O3) is a secondary atmospheric pollutant known to cause negative effects on vegetation in terms of physiological oxidative stress, growth rate reductions and yield losses. In recent years, dose-response relationships based on the O3 stomatal flux and effects on the biomass growth have been defined for several crop species. This study was aimed at developing a dual-sink big-leaf model for winter wheat (Triticum aestivum L.) to map the seasonal Phytotoxic Ozone Dose above a threshold of 6nmolm−2s−1 (POD6) in a domain centered on the Lombardy region (Italy). The model runs on local measured data of air temperature, relative humidity, precipitation, wind speed, global radiation and background O3 concentration provided by regional monitoring networks, and includes parameterizations for the crop's geometry and phenology, the light penetration within the canopy, the stomatal conductance, the atmospheric turbulence, and the soil water availability for the plants. For the 2017 an average POD6 of 2.03mmolm−2PLA (Projected Leaf Area) was found for the Lombardy regional domain, corresponding to an average relative yield loss of 7.5%, using the finest spatio-temporal resolution (1×1km2 and 1-h). An analysis of the model's response to different spatio-temporal resolutions (from 2×2 to 50×50km2 and from 1 to 6 h) suggests that coarser resolution maps underestimated the average POD6 regional value from 8to16%, and were unable to detect O3 hotspots. Nevertheless, resolutions of 5×5km2 1-h, and 1×1km2 3-h, can still be considered reliable for the estimation of the O3 risk at the regional level since they presented relatively low root mean squared error. Furthermore, although temperature was the main limiting factor for the wheat stomatal conductance in most of the domain, soil water availability emerged as the key factor for determining the spatial patterns of the POD6.

Evaluating the Potential of High-Resolution Visible Remote Sensing to Detect Shiraz Disease in Grapevines

Background and Aims. Shiraz disease (SD) is a viral disease associated with Grapevine virus A that causes significant yield loss in economically important grape cultivars in Australia such as Shiraz and Merlot. Current diagnostic methods are time-consuming and costly. This study evaluates an alternative methodology using visible remote sensing imagery to detect SD in Shiraz grapevines. Methods and Results. High-resolution visible remote sensing images were captured of Shiraz grapevines in two South Australian viticultural regions over two seasons. The projected leaf area (PLA) of individual grapevines was estimated from the images. Virus-infected vines had significantly lower PLA than healthy vines in the early season but fewer difference after veraison. The lower PLA was only observed in grapevines coinfected with grapevine leafroll-associated viruses (GLRaVs) and Grapevine virus A (GVA). Shiraz vines infected with either GLRaVs or GVA had similar PLA to healthy vines. Conclusions. High-resolution RGB remote sensing technology has the potential to rapidly estimate SD infection in Shiraz grapevines. Our observations of shoot devigouration only in coinfected vines calls into question the etiology of SD. Further validation of the PLA technique incorporating different regions, seasons, cultivars, and combinations of viruses is needed for improving the robustness of the method. Significance of the Study. This preliminary study presents a new rapid and low-cost surveillance method to estimate SD infections in Shiraz vineyards, which could significantly lower the cost for growers who conduct on-ground SD visual assessments or lab-based tissue testing at the vineyard scale.

Applicability of Variable-Rate Nitrogen Top Dressing Based on Measurement of the Within-Field Variability of Soil Nutrients for Cabbage Production

To improve the efficiency of nitrogen (N) fertilization, it is necessary to perform rapid direct measurements in the field rather than time-consuming laboratory-based chemical analysis. Herein, crop and soil data from the early stages of cabbage growth were acquired through two fall cultivations. Chlorophyll meter value, height, and projected leaf area were evaluated as crop indicators. A positive correlation was observed between the projected leaf area or its rate of increase 2–3 weeks after transplantation and head fresh weight (FW). After comparing two water-content reflectometers (WCR) and a nitrate sensor, we selected a WCR with a 12 cm-long rod as the soil indicator. The diagnostic method was verified using varying amounts of N basal fertilizer during spring cultivation. The variable rate of N top dressing (25, 50, and 75% total N) based on the electrical conductivity (EC) 14 days after transplantation reduced the subsequent EC variability. No differences in head FW were observed between the treatments. A 25% reduction in N fertilizer was considered possible for half of the plots. The quantity of inorganic N extracted by potassium chloride from the crop soil after cultivation was unaffected by the amount of N fertilizer. Therefore, the diagnostic method proposed herein is suitable for soil N management.

Phenotyping drought tolerance and yield performance of barley using a combination of imaging methods

Drought resistance represents a complex of traits that are differently employed depending on drought type, severity and timing. Hence, a relatively comprehensive assessment of morphological and physiological phenotypic response to drought is required. We evaluated the dynamic responses of six barley genotypes, representing a wide range of drought tolerance, to continuous drying and re-watering using non-invasively measured parameters based on red-green-blue (RGB), thermal infrared and chlorophyll fluorescence imaging within an automated phenotyping platform. We identified three critical points in drought progress: i) 50% level of available soil water, ii) wilting point, iii) full plant recovery after re-watering. However, the individual monitored parameters showed the potential to evaluate drought sensitivity at different points since the onset of drying. The correlation with relative yield response gradually increased for the side projected leaf area (SPA) and reached the maximum at the point of full recovery. The actual quantum yield of photosystem II (ΦPSII) showed the highest correlation with a relative grain yield around wilting point. In contrast, the relative leaf temperature difference demonstrated a high correlation with yield response earlier, at 50% of available water. The highest correlations with the relative yield response were obtained for the colour RGB analysis at the wilting point and after recovery, particularly for khaki, beige, dark-green and olive-green colours. Multiple regression with parameters providing Pearson’s correlation coefficient R > 0.5 slightly improved the estimation of relative yield response to drought but ensured significant improvement of absolute grain yield estimation under drought stress. This study shows that combining the phenotyping methods representing different morphological and physiological traits allows not only the assessment of drought tolerance (based on relative yield response to drought) which is crucial for selecting the genetic resources for the subsequent breeding process but also allows to test the yield performance of new genotypes under drought stress.

Calculation method of wilting index based on fractal dimension of multispectral images for the soybean canopy

Drought stress is an important environmental factor that restricts the quality and high yield of the soybean. Rapid and nondestructive monitoring of canopy traits under drought stress is very critical to the cultivation and quality improvement of for the soybean plants. To solve the problems of traditional artificial chemical tests in determining soybean wilt index, such as cumbersome, time-consuming, and laborious, a method for calculating wilting index was proposed based on the fractal dimension of multispectral images for the soybean canopy. First, the soybean variety of Suinong 26 was chosen as the experimental subject, the multispectral images of five channels (RGB, GRE, NIR, RED and REG) of the soybean canopy were acquired. Second, the soybean canopy image was extracted by K-means clustering algorithm from the original multispectral image, with an effective segmentation rate (ESR) of 0.9729. Third, on the base of the multispectral image enhanced by the gamma transform for the soybean canopy, a method for calculating wilting index (LBC,LDBC, LDB) was constructed for the soybean canopy by using fractal dimension algorithms including box-counting (BC) dimension, differential box-counting (DBC) dimension and double blanket (DB) dimension. Finally, the proposed method was proved by using the average leaf inclination (LI) and the projected leaf area (PLA). The experimental results showed that the determination coefficients R2 were all above 0.85. The LBC got the highest correlation with LI and PLA in the RGB channel with coefficient determination R2 above 0.8729. The LDB got the highest correlation with LI and PLA in the GRE, NIR, and REG channels with determination coefficient R2 above 0.8824. The LDBC got the highest correlation with LI and PLA in the RED channel with determination coefficient R2 above 0.8857. This achievement presented that the proposed wilting index could characterize the change degree for the ecology and morphology when soybeans were affected by drought stress, which can provide the technical support to scientifically monitor the phenotypic traits in the process of variety breeding, field cultivation for the soybean plant.

Local CO2 Application within Strawberry Plant Canopy Increased Dry Matter Production and Fruit Yield in Summer and Autumn Culture

ABSTRACT June-bearing strawberry cultivars have been widely grown in Japan. Since they are harvested in winter and spring, little produce is available in summer and autumn. To achieve stable year-round supply, we need to expand the production area of ever-bearing cultivars. Here, we examined whether it is possible to increase dry matter (DM) production and fruit yield by increasing the CO2 concentration within the plant canopy through local application to promote photosynthesis in summer and autumn protected culture. We investigated the CO2 concentration in the plant canopy, DM production characteristics, yield characteristics, fruit quality, projected leaf area, cumulative light interception, and light use efficiency. We confirmed that the CO2 concentration within the plant canopy could be increased in summer and autumn (Control; 398 ppm, CO2; 1280 ppm), significantly increasing DM production and total yield (Control; 349 g, CO2; 447.5 g). We consider that local application of CO2 increased the projected leaf area and thus cumulative light interception. This method may help to increase fruit yield in summer and autumn protected culture.

LIDAR-Based Phenotyping for Drought Response and Drought Tolerance in Potato

AbstractAs climate changes, maintenance of yield stability requires efficient selection for drought tolerance. Drought-tolerant cultivars have been successfully but slowly bred by yield-based selection in arid environments. Marker-assisted selection accelerates breeding but is less effective for polygenic traits. Therefore, we investigated a selection based on phenotypic markers derived from automatic phenotyping systems. Our trial comprised 64 potato genotypes previously characterised for drought tolerance in ten trials representing Central European drought stress scenarios. In two trials, an automobile LIDAR system continuously monitored shoot development under optimal (C) and reduced (S) water supply. Six 3D images per day provided time courses of plant height (PH), leaf area (A3D), projected leaf area (A2D) and leaf angle (LA). The evaluation workflow employed logistic regression to estimate initial slope (k), inflection point (Tm) and maximum (Mx) for the growth curves of PH and A2D. Genotype × environment interaction affected all parameters significantly. Tm(A2D)s and Mx(A2D)s correlated significantly positive with drought tolerance, and Mx(PH)s correlated negatively. Drought tolerance was not associated with LAc, but correlated significantly with the LAs during late night and at dawn. Drought-tolerant genotypes had a lower LAs than drought-sensitive genotypes, thus resembling unstressed plants. The decision tree model selected Tm(A2D)s and Mx(PH)c as the most important parameters for tolerance class prediction. The model predicted sensitive genotypes more reliably than tolerant genotype and may thus complement the previously published model based on leaf metabolites/transcripts.

Optimized Excess-Green Image Binarization for Accurate Estimation of Lettuce Seedling Leaf-Area in a Plant Factory

Real-time, continuous young seedling-growth measurement improves plant factory stabilization and productivity. Projected leaf area (PLA) based on seedling top-view images is a useful growth index, and easy to measure continuously for large seedling populations. However, it is difficult to automatically determine PLA with a high degree of accuracy, because RGB image color-balance fluctuates with plant growth, leaf movement, and environment. Therefore, we developed a technique for determining PLA on nursery-grown lettuce seedlings. Using a Raspberry Pi 3 microcomputer with a camera module placed above the seedlings, RGB images of 153 seedlings were obtained every 20 min from day 6 to 15 after sowing. Seedling PLA images were obtained by binarization and separation of the leaves from the background. To assess binarization accuracy, we used an Intersection over Union (IoU) index to compare the standard Excess Green (ExG) method, an optimized ExG method (O-ExG), and the artificial neural network U-Net method. Results showed that O-ExG was optimal under the experimental conditions tested. PLA and circadian rhythm amplitude extracted from PLA-image time-series data were independent, implying they can be used together for growth prediction. These findings improve the accuracy of imagebased growth prediction and have practical application in plant factories.

Leafamine®, a Free Amino Acid-Rich Biostimulant, Promotes Growth Performance of Deficit-Irrigated Lettuce.

Water deficit causes substantial yield losses that climate change is going to make even more problematic. Sustainable agricultural practices are increasingly developed to improve plant tolerance to abiotic stresses. One innovative solution amongst others is the integration of plant biostimulants in agriculture. In this work, we investigate for the first time the effects of the biostimulant –Leafamine®–a protein hydrolysate on greenhouse lettuce (Lactuca sativa L.) grown under well-watered and water-deficit conditions. We examined the physiological and metabolomic water deficit responses of lettuce treated with Leafamine® (0.585 g/pot) or not. Root application of Leafamine® increased the shoot fresh biomass of both well-watered (+40%) and deficit-irrigated (+20%) lettuce plants because the projected leaf area increased. Our results also indicate that Leafamine® application could adjust the nitrogen metabolism by enhancing the total nitrogen content, amino acid (proline) contents and the total protein level in lettuce leaves, irrespective of the water condition. Osmolytes such as soluble sugars and polyols, also increased in Leafamine®-treated lettuce. Our findings suggest that the protective effect of Leafamine is a widespread change in plant metabolism and could involve ABA, putrescine and raffinose.

Substantial yield reduction in sweet potato due to tropospheric ozone, the dose-response function

Impacts of tropospheric ozone on sweet potato (Ipomoea batatas) are poorly understood despite being a staple food grown in locations deemed at risk from ozone pollution. Three varieties of sweet potato were exposed to ozone treatments (peaks of: 30 (Low), 80 (Medium), and 110 (High) ppb) using heated solardomes. Weekly measurements of stomatal conductance (gs) and chlorophyll content (CI) were used to determine physiological responses, along with final yield. gs and CI were reduced with increasing ozone exposure, but effects were partially masked due to elevated leaf senescence and turnover. Yield for the Erato orange and Murasaki varieties was reduced by ∼40% and ∼50% (Medium and High ozone treatments, respectively, vs Low) whereas Beauregard yield was reduced by 58% in both.The DO3SE (Deposition of Ozone for Stomatal Exchange) model was parameterized for gs in response to light, temperature, vapour pressure deficit and soil water potential. Clear responses of gs to the environmental parameters were found. Yield reductions were correlated with both concentration based AOT40 (accumulated ozone above a threshold of 40 ppb) and flux based POD6 (accumulated stomatal flux of ozone above a threshold of 6 nmol m− 2 s− 1) metrics (R2 0.66 p = 0.01; and R2 0.44 p = 0.05, respectively). A critical level estimate of a POD6 of 3 (mmol m−2 Projected Leaf Area−1) was obtained using the relationship. This study showed that sweet potato yield was reduced by ozone pollution, and that stomatal conductance and chlorophyll content were also affected. Results from this study can improve model predictions of ozone impacts on sweet potato together with associated ozone risk assessments for tropical countries.