Plant Growth Analysis Research Articles

Biochar: effects on crop growth

Excessive tonnage of agricultural wastes from annual and field crops are generated yearly in Malaysia. The improper treatment such as open burning of residual biomass after the harvesting of rice plantcan affect global warming. The processing of agricultural wastes into biochar through pyrolysis has gained tremendous interest globally. A field trial to compare effectiveness on rice growth of variety MR 263 using rice husk and paddy straw biochar was determinedby application of four different rates of biochar 300 g, 600 g, 900 g and control (without biochar). Plant growth analysis based on average plant height was optimum with addition of 900 g rice husk biochar. A greenhouse trial using oil palm and rubber seedlings as indicatorswith application of 0, 10, 20, 30, and 40 g rice husk biochar and paddy straw biochar to the soil showed the highest plant height for treatment with 10 g biochar, ranging from 64.7 to 68.9 cm. Plant heights were slightly lower at higher levels of biochar added. From this study, application of biochar to the growth medium increased soil fertility as indicated by growth enhancement in soils enriched with rice husk biochar and paddy straw biochar.

Tensor-based classification and segmentation of three-dimensional point clouds for organ-level plant phenotyping and growth analysis

Plant breeding is the key to genetic improvement and increased crop yield. Breeding projects require the determination of plant phenotypes (i.e., phenotyping), a task that becomes tedious and biased when performed manually. Thus, to speed-up and improve breeding projects, novel autonomous phenotyping methods must be developed. Among the currently available methods, three-dimensional (3-D) plant modeling offers a high-resolution description of plant morphology and hence a detailed analysis of plant growth. However, advanced organ-level scale analysis of 3-D data is a complex process that requires preliminary segmentation of the 3-D point cloud into leaf- and stem-related points. Despite their obvious importance, only a few segmentation models have been reported, and many studies still perform this task manually. This paper presents a novel data-driven segmentation model as a basis for organ-level plant analysis. It shows that a point-wise computation of first- and second-order tensors is sufficient to characterize the data, without the need for pre-defined shape assumptions and constraints. Application of the model to a variety of species at different growth stages gave accurate segmentation results regardless of the species morphology and the plant growth stage. The derived information provides an organ-level phenotyping ability, and the paper shows how this ability remains accurate for various geometries and growth stages, even when the model is applied to noisy data or when the reconstruction is incomplete. An accurate, nondestructive, and autonomous organ-level phenotyping pipeline, such as the one described here, will find application for trait selection processes in breeding programs. In addition, the short processing time facilitate the use of our pipeline for precision agriculture field applications.

Automated phenotyping for early vigour of field pea seedlings in controlled environment by colour imaging technology.

Early vigour of seedlings is a beneficial trait of field pea (Pisum sativum L.) that contributes to weed control, water use efficiency and is likely to contribute to yield under certain environments. Although breeding is considered the most effective approach to improve early vigour of field pea, the absence of a robust and high-throughput phenotyping tool to dissect this complex trait is currently a major obstacle of genetic improvement programs to address this issue. To develop this tool, separate trials on 44 genetically diverse field pea genotypes were conducted in the automated plant phenotyping platform of Plant Phenomics Victoria, Horsham and in the field, respectively. High correlation between estimated plant parameters derived from the automated phenotyping platform and important early vigour traits such as shoot biomass, leaf area and plant height indicated that the derived plant parameters can be used to predict vigour traits in field pea seedlings. Plant growth analysis demonstrated that the “broken-stick” model fitted well with the growth pattern of all field pea genotypes and can be used to determine the linear growth phase. Further analysis suggested that the estimated plant parameters collected at the linear growth phase can effectively differentiate early vigour across field pea genotypes. High correlation between normalised difference vegetation indices captured from the field trial and estimated shoot biomass and top-view area confirmed the consistent performance of early vigour field pea genotypes under controlled and field environments. Overall, our results demonstrated that this robust screening tool is highly applicable and will enable breeding programs to rapidly identify early vigour traits and utilise germplasm to contribute to the genetic improvement of field peas.

The Power Grid Development with Distributed Wind Plant Growth Based on Coordination Analysis

the wind plant capacity developed continuously since 2009. However, the wind plant type changed quietly, those preformed in the unit capacity increased from 1MW to 5MW, the wind farm located from onshore wind power to offshore wind power, and the large-scale distributed wind plant transformed into small-scale distributed one. Recently, the distributed wind plant was growing rapidly for policy guidance, which has a great influence on the power grid operation because of the change of the power supply layout. How to analyze the influence is an important matter to power system development. The analyzed results will decide the trends of the distributed wind plant in future. Therefore, the coordination analysis of distribute wind plant growth is crucial to those developments. In this paper, the reasonable distributed generation scale arrangement was discussed based on the difference measures.

Modelling plant yield and quality response of fresh-market spinach (Spinacia oleracea L.) to mineral nitrogen availability in the root zone

Spinach is one of the most important green-leafy vegetables, consumed worldwide, and its intake is beneficial for human beings. In this crop, produce yield and quality are closely related to plant nitrogen (N) nutrition. A precise supply of N is also essential for high environmental and economic sustainability. Main aims of the work were: i) to establish relationships between produce yield or quality and mineral N availability in the root zone; and ii) to define an optimal mineral N level to be maintained in the root zone for spinach. Eight experiments were carried out during a four-year-long period under typical Mediterranean climate conditions. Different amounts of N fertilisers were supplied leading to twenty different levels of mineral N in the root zone. Experimental measurements included climate parameters, plant growth, tissue and soil analyses, produce yield and quality indicators. A segmented linear model significantly represented the relationship between crop yield (1.7 to 21.7 t ha–1) and soil mineral N concentration (7.6 to 41.0 mg kg–1). Basing on this model, an optimal mineral N threshold was fixed at 23.4 mg kg–1. Above this threshold, crop yield did not show any significant variations as well as tissue characteristics and produce quality. Plants grown under suboptimal N levels showed reduction in growth, tissue mineral (nutrients) content, and SPAD index. The proposed models could be implemented in fertilisation protocols for the optimization of N supply and the estimation of spinach growth and yield.

Interaksi Tanaman Kacang Tanah (Arachis hypogeae) dalam Agroforestri Interaction Of Peanuts (Arachis hypogeae) In Agroforestri

Nutrient availability in the forest soils is affected by forest degradation and deforestation as forest conversion. Peanut (Arachis hypogaea) is included LeguminalesOrdo and able to supply nutrient from the plant parts. As intercrops in agroforestry patterns, peanuts provide interaction for the surrounding soil and plants. The purpose of this research is to know the interaction of peanut plants on nutrient availability and on the growth of rubber plant (Heveabrasiliensis Muell, Arg.) on agroforestry pattern. This research was conducted at the Banjarbaru through the observation of rubber plant growth and soil analysis. Research result shows that peanuts can increase the availability of nutrients in the soil. The N element increases from 0.16% to 0.45%, thereby stimulating vegetative growth (green) such as leaves. The P element increases from 5.57 mg/100gr to 8.93 mg/100gr, the K element increases from 15.87 mg/100gr to 41.50 mg/100 gr, and organic C increased from 0.83% to 1.55%. The interaction of peanut crops as intercropping plants was able to give high impact to the height of the rubber plant, namely 77.68 cm. The impact on the growth of rubber plant diameter also increased by 0.59 cm compared to only the rubber pattern of 0.28 cm.

An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.

A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

Morpho-physiological adaptation of Jatropha curcas L. to salinity stress

The aim of the current study is to assess the growth response, biochemical changes, photosynthetic pigments content and gas exchanges of physic nut (Jatropha curcas) grown under natural saline conditions. The experiment was arranged in a completely randomized design based on the following soil electrical conductivities: 0.29 dS.m-1 (control), 1.76 dS.m-1 (moderate salt concentration), 2.61 dS.m-1 (high salt concentration), 3.79 dS.m-1 (very high salt concentration). Physic nut plants were kept under saline conditions for 19 days in greenhouse. Plant growth analyses were performed on a weekly basis. Plant biomass allocation was quantified at the end of the experiment. Leaf gas exchange, stomatal conductance, Fv/Fm and quantum yield were quantified 102 days after planting. Photosynthetic pigments, amino acids, proline and carbohydrates in fresh leaf tissue were also quantified. The leaf antioxidant enzymes catalase (CAT) and ascorbate peroxidase (APX) were quantified. Although there was no alteration in biomass allocation, the initial growth of J. curcas was gradually reduced by increasing salt concentration, which was observed through reduced plant height, stem diameter, and total number of leaves. Soil electrical conductivity 3.79 dS.m-1 was lethal to seedlings. Seedlings exposed to salt stress had their photosynthesis, stomatal conductance, transpiration and effective photochemical efficiency reduced, and their catalase and ascorbate peroxidase enzyme activity increased. Amino acids, proline and carbohydrate concentrations increased due to salt stress, whereas there was decrease in the chlorophyll content. J. curcas is sensitive to soil salinity at electrical conductivity levels higher than 1.76 dS.m-1. To some extent, salinity effects can be relieved by osmolyte accumulation and by greater antioxidant activity; however, these factors were not sufficient to keep plant growth within normal rates.

Constructed wetlands for phytoremediation of industrial wastewater in Addis Ababa, Ethiopia

Brewery industries generate large amounts of wastewater rich in organic matter originating from the brewing process, and they are among the major polluting industries. This study aimed to assess the phytoremediation of brewery wastewater using horizontal subsurface flow constructed wetlands (HSFCWs) vegetated with Typha latifolia and Pennisetum purpureum for organics removal and plant growth analysis. Six parallel pilot-scale HSFCWs were constructed and operated to assess potential of treating wastewater sourced from St. George brewery factory located in Addis Ababa, Ethiopia. Three units were planted with T. latifolia and the other three with P. purpureum with one control without plants for each species. Primarily settled wastewater was fed evenly to them by gravity. Wastewater quality, plant growth analysis and system efficiency were observed during the experiment following standard methods. Both plants grew and established well, however, T. latifolia had more biomass and vigorous growth and showed good phytoremedial capacity to remove organic pollutants. Average removal efficiencies for BOD5 and COD were significant (p < 0.05), up to 87% (inlet BOD5 of 748–1642 mg l−1) and up to 81% (inlet COD of 835–2602 mg l−1) and T. latifolia slightly outperformed P. purpureum. Estimated biomass of significant (p < 0.05) value (0.61–0.86 kg DW m−2) was produced. HSFCWs are green and environmentally sustainable technology that offers promising alternative wastewater treatment method in developing countries of tropical climate due to its low-tech nature. Integrating treatment and biomass production needs further improvement.

Application of Rhizobium-Pseudomonas consortia for enhanced production of mungbean in sustainable manner

In this study plant growth promoting rhizobacteria (PGPR) were isolated from root nodules of Vigna radiata and rhizospheric soil of Abrus precatorius respectively. On the basis of morphological, biochemical and physiological characterization, isolate R5 (from root nodules of V. radiata) was identified as Rhizobium and P21 (from rhizosphere of A. precatorius) as Pseudomonas. Isolates R5 and P21 showed positive results for phosphate solubilization, zinc solubilization, nitrogen fixation, production of indole acetic acid (IAA), siderophore and exopolysaccharides (EPS). Isolates (R5 and P21) were applied as coinoculant on mungbean (V. radiata) to check their impact on its growth in laboratory conditions and pot trials. Seed germination rate and growth analysis of plants were observed by measuring growth parameters (root length, shoot length, leaves count, root nodule count, fresh weight, dry weight and chlorophyll content). It was observed that R5 and P21 significantly enhanced the plant growth in comparison to control (without treatment), but consortial inoculation was more effective in comparison to mono-inoculation of R5 and P21.

Generation of ultrasonic waves in vegetable tissue via non-ablative laser pulses

The study of vegetable tissue is essential in plant growth analysis. The elasticity is an important characteristic when firmness is studied in fruits or when turgidity is tested in plant leaves. This property gives information about the ripeness of fruits, the water content in leaves, and, in general, the morphological and physiological state of plants. Hence, the propagation of elastic waves in these media is a useful tool that gives information about the developmental stage of the plant. In this sense, laser ultrasonics is a promising method in the characterization of composite materials. In a certain way, vegetable tissues can be thought of as a kind of biological composite abstracted as a stratified plate of several layers. In this work, as a first approximation, we propose a methodology to generate guided waves in vegetable tissues by using laser ultrasonics techniques. We implement a pulsed laser system to induce ultrasonic waves without damage on the samples. Besides, we develop a methodology for processing the propagated acoustic waves. This analysis allows us the assessment of dispersive properties, a useful tool when determining important descriptors such as firmness index, water content, and provide useful information to feed biophysical models, among others.

Mixed-Effects Estimation in Dynamic Models of Plant Growth for the Assessment of Inter-individual Variability

Modeling inter-individual variability in plant populations is a key issue to understand crop heterogeneity and its variations in response to the environment. Being able to describe the interactions among plants and explain the variability observed in the population could provide useful information on how to control it and improve global plant growth. We propose here a method to model plant variability within a field, by extending the so-called GreenLab functional-structural plant model from the individual to the population scale via nonlinear mixed-effects modeling. Parameter estimation of the population model is achieved using the stochastic approximation expectation maximization algorithm, implemented in the platform for plant growth modeling and analysis PyGMAlion. The method is first applied on a set of simulated data and then on a real dataset from a population of 34 winter oilseed rape plants at the rosette stage. Results show that our method allows for a good characterization of the variability in the population with only a limited number of parameters, which is a key point for plant models. Results on simulated data show that parameters associated with a low sensitivity index are inaccurately estimated by the algorithm when considered as random effects, but a good stability of the results can be obtained by considering them as fixed effects. These results open new ways for the analysis of inter-plant variability within a population and the study of plant–plant competition.Supplementary materials accompanying this paper appear online.

Quantile regression of nonlinear models to describe different levels of dry matter accumulation in garlic plants

ABSTRACT: Plant growth analyses are important because they generate information on the demand and necessary care for each development stage of a plant. Nonlinear regression models are appropriate for the description of curves of growth, since they include parameters with practical biological interpretation. However, these models present information in terms of the conditional mean, and they are subject to problems in the adjustment caused by possible outliers or asymmetry in the distribution of the data. Quantile regression can solve these problems, and it allows the estimation of different quantiles, generating more complete and robust results. The objective of this research was to adjust a nonlinear quantile regression model for the study of dry matter accumulation in garlic plants (Allium sativum L.) over time, estimating parameters at three different quantiles and classifying each garlic accession according to its growth rate and asymptotic weight. The nonlinear regression model fitted was a Logistic model, and 30 garlic accessions were evaluated. These 30 accessions were divided based on the model with the closest quantile estimates; 12 accessions were classified as of lesser interest for planting, 6 were classified as intermediate, and 12 were classified as of greater interest for planting.

In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR.

Plant breeding programs and a wide range of plant science applications would greatly benefit from the development of in-field high throughput phenotyping technologies. In this study, a terrestrial LiDAR-based high throughput phenotyping system was developed. A 2D LiDAR was applied to scan plants from overhead in the field, and an RTK-GPS was used to provide spatial coordinates. Precise 3D models of scanned plants were reconstructed based on the LiDAR and RTK-GPS data. The ground plane of the 3D model was separated by RANSAC algorithm and a Euclidean clustering algorithm was applied to remove noise generated by weeds. After that, clean 3D surface models of cotton plants were obtained, from which three plot-level morphologic traits including canopy height, projected canopy area, and plant volume were derived. Canopy height ranging from 85th percentile to the maximum height were computed based on the histogram of the z coordinate for all measured points; projected canopy area was derived by projecting all points on a ground plane; and a Trapezoidal rule based algorithm was proposed to estimate plant volume. Results of validation experiments showed good agreement between LiDAR measurements and manual measurements for maximum canopy height, projected canopy area, and plant volume, with R2-values of 0.97, 0.97, and 0.98, respectively. The developed system was used to scan the whole field repeatedly over the period from 43 to 109 days after planting. Growth trends and growth rate curves for all three derived morphologic traits were established over the monitoring period for each cultivar. Overall, four different cultivars showed similar growth trends and growth rate patterns. Each cultivar continued to grow until ~88 days after planting, and from then on varied little. However, the actual values were cultivar specific. Correlation analysis between morphologic traits and final yield was conducted over the monitoring period. When considering each cultivar individually, the three traits showed the best correlations with final yield during the period between around 67 and 109 days after planting, with maximum R2-values of up to 0.84, 0.88, and 0.85, respectively. The developed system demonstrated relatively high throughput data collection and analysis.

Impact of water management on root morphology, growth and yield component of lowland rice varieties under the organic system of rice intensification

Water management is one of the keys to success in increasing rice production in paddy fields. Paddy rice production will decrease if the rice plants suffer from water stress, and conversely conventional cultivation is very wasteful of water. Local aromatic varieties have deeper rooting character than improved varieties making it more efficient in using water. The purpose of this study was to obtain more efficient and more productive aromatic local varieties in the use of irrigation water in the SRI organic cultivation system. This research was conducted in organic rice fields in Kebonagung village, Imogiri, Bantul with split plot design of 2 factors with 4 replications. Factor I: The mode of irrigation consists of (a) intermittent irrigation, and (b) continuous irrigation and Factor II: varieties consisting of (a) Mentikwangi, (b) Gabusan, (c) Sintanur and (d) IR64. The observation parameters included (1) root characters i.e. surface area of root, total length of root, and dry weight of root, (2) plant growth analysis i.e. specific leaf weight, net assimilation rate, plant growth rate, (3) and yield components i.e. weight 1000 grains, grain weight per hill, and grain weight per plot. The results showed that intermittent irrigation increased the surface area of root and the dry weight of root of Mentikwangi variety, and increased the specific leaf weight and weight of 1000 grains of IR64 variety, and increased the specific leaf weight and the net assimilation rate of Sintanur variety. Grain weight per hill and per plot showed no difference between intermittent irrigation and continuous irrigation. Grain weight per hill and per plot in intermittent irrigation achieved by Gabusan variety were 35.69 g and 14.28 kg respectively, while in continuous irrigation the grain weight achieved by Gabusan variety were 37.69 g and 15.08 kg respectively. In conclusion, there was no difference in yield between intermittent irrigation and continuous irrigation, so that the use of intermittent irrigation is more efficient in water use.

The Possibility of Using Digital Images in Assessment of Plant Canopy Development and Weed Spread

AbstractNowadays, there are various methods of plant biomass assessment available for the purposes of plant growth analysis. Visual plant coverage assessment is often subjective; the other methods are destructive or require purchasing some special devices. This paper presents the assessment of the possibilities and limitations of using digital images made by conventional digital cameras for the purposes of monitoring of the plant canopy development and weed distribution during a vegetation period using the example from the field experiment established on agricultural land in Malanta at the experimental site of the Slovak University of Agriculture. The study is focused on assessment of the effect of biochar application on gas emission, hydrophysical soil properties as well as plant response and yields and it was established in the spring of 2014. Downward images of corn (Zea maysL.) were taken during four sampling campaigns in the vegetation season 2015. Images were analysed by the BreedPix software that could estimate the portion of green fraction (count of green pixels) and thus the image-derived vegetation index (IDVI). According to the image analysis of photos taken during different sampling dates, it could be concluded that biochar addition had a positive effect on the plant growth (above ground biomass) since all treatments resulted in higher IDVIs at the end of the vegetative growth in comparison to control. Further, we assume that the increasing trend in the crop canopy growth was partially limited by competitive presence of weeds at the beginning of the study. According to our experience, we can recommend the software for temporal and spatial monitoring of agricultural crops development. The usage is limited to early growth stages. Moreover, it can be also used for assessment of the weed coverage.

Low temperature limits for root growth in alpine species are set by cell differentiation.

Plant growth in cold climates is not limited by carbon assimilation (source activity) but rather by reduced carbon investment into new tissues (sink limitation). It has been hypothesized that all cold-adapted plants face similar growth constraints at low temperature mainly associated with the formation of new tissues. To explore the thermal limitation of plant tissue formation, we studied root growth and anatomical root tissue characteristics in four cold-adapted alpine species (Ranunculus glacialis, Rumex alpinus, Tussilago farfara, Poa alpina), grown in thermostated soils with a vertical temperature gradient approaching 1 °C. Above-ground plant organs were exposed to typical alpine climate conditions (high solar radiation and cool nights) at 2440 m a.s.l. in the Swiss Alps to assure continuous source activity. Image-based measurements of root growth (root elongation rates at 12-h intervals, RERs) were combined with anatomical examinations in thermally constrained root tips as well as with a functional growth analysis of entire plants. Temperatures in the range 0.8 to 1.4 °C were denoted as critically low temperature thresholds for root formation across the four species. The RERs per 12 h revealed that roots kept extending at low rates at 0.7-1.2 °C but cell elongation and xylem lignification were clearly inhibited in the terminal zones of root tips. Roots exposed to temperatures between 1 and 5 °C showed strongly reduced elongation rates so that these roots contributed very little to the entire root system compared to control roots grown at 10 °C. Hardly any secondary roots were formed at temperatures below 5 °C and total root mass was substantially lower (74 % reduction in comparison to control), also the above-ground biomass was reduced by 23 %. Cell elongation and differentiation rather than cell division control length and shape of root cells at the low temperature limit of growth. Lignification of root xylem is clearly constrained at temperatures below 3 °C.

Analysis of Plant Growth of Ten Varieties of Sweet Potato (Ipomoea batatas L.) Cultivated in Rainy Season

Analysis of Plant Growth of Ten Varieties of Sweet Potato (Ipomoea batatas L.) Cultivated in Rainy Season

Plant growth analysis system: a new approach for greenhouse management and horticultural research

Plant growth analysis system: a new approach for greenhouse management and horticultural research

Analysis of plant growth and biochemical parameters in Amsonia orientalis after in vitro salt stress

This study aimed to better understand the limited natural distribution of the endangered ornamental plant Amsonia orientalis Decne. by focusing on salt stress, a common limiting factor of plant growth. Plants were subjected to in vitro salt stress at concentrations between 25 and 150 mM. In general, shoot and root lengths, root number, and total protein, chlorophyll a and carotenoid content were negatively influenced at NaCl concentrations above 25 mM. Hydrogen peroxide, malondialdehyde and proline content all gradually increased with increasing salt concentration. Activity levels of the antioxidant enzymes catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) were all at their maximum in plants cultured in medium containing 50 mM NaCl. Compared to control cultures, an overall upward trend in POD activity was observed with increasing salt concentration, while the activity levels of SOD and CAT increased at lower concentrations but were limited at elevated concentrations of NaCl. These results suggest that A. orientalis prefers soils with no or very low salt but can tolerate NaCl up to a concentration of 50 mM.