Leaf Count Research Articles

Evaluating cauliflower (Brassica oleracea) production under different fertilizer systems

The field experiment was carried out in the open field conditions during 2019–20 growing season at Palestine Technical University–Kadoorie (PTUK), Palestine farm to determine the effect of different fertilizer regimes comprised of organic fertilizer (compost), chemical fertilizers and control (without fertilizer application) on cauliflower production. Chlorophyll content (SPAD), shoot and root wet weight, number of leaves and plant height were investigated. Compost treatments tended to produce significantly the highest shoot, root wet weight, leaf counts, plant height and chlorophyll content compared to either chemical fertilizers or the control. Significantly higher plant vigour of cauliflower was noticed under compost treatments followed by chemical fertilizers application and control treatment.

Comparative effect of Bambusa vulgaris Schrad. and Gliricidia sepium(Jacq.) charcoal powders on the growth of Parkia biglobosa (Jacq.) Benth seedlings

The high cost of purchasing inorganic fertilizer and its impacts on the soil and the environment remains a major concern in environmental management. Consequently, there is the need to explore other ways to enhance soil fertility. The study investigated the effects of Bambusa vulgaris Schrad. and Gliricidia sepium (Jacq) charcoal powders on the growth of Parkia biglobosa (Jacq). Benth seedlings. The experiment was laid out in a Completely Randomized Design (CRD) with 13 treatments and 9 replicates each. Seeds were sowed directly into polyethene pots while the various treatments were added a week after sowing seeds. Watering was done daily while weeding was done periodically. Growth parameters of seedling height (cm), stem diameter (mm) and leaf count were assessed weekly for 16weeks. Data collected was subjected to Analysis of Variance (ANOVA) at 5% level of significance while the means were separated using Duncan Multiple Range Test (DMRT). The result obtained showed that 15g of Gliricidia sepium charcoal powder + 2kg of topsoil) performed best in height and leaf production with values of 15.85cm and 20.58 respectively.5g of Bambusa vulgaris charcoal powder + 2kg of topsoil) performed best with the value of 0.41mm for stem diameter.2kg of topsoil had the least performance for all parameter assessed. The ANOVA result showed that there was significant difference at P>0.5 among the treatments at 5% level of probability in terms of plant height, stem diameter, leaf production. The study concludes that the addition of charcoalas soil additive enhanced growth of Parkia biglobosa seedling at nursery stage.

Response of Different Potato Genotypes to Drought Stress

A pot trial was established to evaluate the response of different potato genotypes to drought stress. Two potato genotypes (Demon and Hopehely) were exposed to two water levels (80% and 50% water holding capacity). The trial was replicated 16 times and 4 replications were harvested at 36 DAS, 54 DAS, 72 DAS, and 90 DAS. The results revealed that drought significantly reduced the growth and plant development of Demon (G1). Hopehely (G2) produced a higher yield in control as well as drought condition. Tuber yield positively correlated with tubers number, and root weight that was significantly higher in Hopehely under both experimental conditions. Hopehely produced a higher number of tubers (18) than Demon whereas Demon produced 11 tubers per plant. Moreover, under drought conditions, the relative water content of leaves and nitrogen content in foliage increased in Hopehely while decreased in Demon. Drought stress caused a 40% reduction in plant height and a 24.3% reduction in the number of leaves in Demon that was significantly higher than the 11% plant height reduction and 9.1% leaf count reduction in Hopehely. It was observed that the morphology of Hopehely (producing dwarf plants, fewer leaves, maintaining water content of leaves, producing more tubers) helped it to be a better drought-tolerant genotype compared to Demon.

A Class of Random Recursive Tree Algorithms with Deletion

We examine a discrete random recursive tree growth process that, at each time step, either adds or deletes a node from the tree with fixed, complementary probabilities. Node addition follows the usual uniform attachment model. For node removal, we identify a class of deletion rules guaranteeing the current tree conditioned on its size is uniformly distributed over its range. By using generating function theory and singularity analysis, we obtain asymptotic estimates for the expectation and variance of a tree’s size, as well as its expected leaf count and root degree. In all cases, the behavior of such trees falls into three regimes determined by the insertion probability. Interestingly, the results are independent of the specific class member deletion rule used.

Effects of Scarification, Phytohormones, Soil Type, and Warming on the Germination and/or Seedling Performance of Three Tamaulipan Thornscrub Forest Species.

The Tamaulipan thornforests of south Texas and northeast Mexico are an ecologically and economically important conservation hotspot. Thornforest restoration is limited by native tree and shrub seedling availability for planting. Seedling shortages arise from low seed availability and knowledge gaps regarding best practices for germinating and growing the 70+ thornforest species desired for restoration plantings. To fill key knowledge gaps, we investigated three ecologically important thornforest species with low or highly variable germination or seedling survival rates: Ebenopsis ebano, Cordia boissieri, and Zanthoxylum fagara. For each, we quantified the effects of different dosages of chemical seed treatments used to promote germination (sulfuric acid, SA; gibberellic acid, GA; indole-3-butyric acid, IBA) on germination likelihood and timing. We also quantified the effects that these chemical seed treatments, soil media mixture type, and soil warming had on seedling survival, growth, and root morphology. Ebenopsis germination peaked (>90%) with 40–60 min SA treatment. Cordia germination peaked (40%) with 100 mg/L GA treatment. Zanthoxylum germination was negligible across all treatments. Seed molding was rare but stirring during SA treatment reduced Ebenopsis molding by 4%. Ebenopsis seedling survival, height, leaf count, and root morphology were minimally affected by seed treatments, generally reduced by warming, and influenced by soil mix, which also mediated responses to warming. These results suggest improvements to existing practices that could increase Ebenopsis germination by 10–20% and potentially double Cordia germination.

Leaf Counting: Fusing Network Components for Improved Accuracy.

Leaf counting in potted plants is an important building block for estimating their health status and growth rate and has obtained increasing attention from the visual phenotyping community in recent years. Two novel deep learning approaches for visual leaf counting tasks are proposed, evaluated, and compared in this study. The first method performs counting via direct regression but using multiple image representation resolutions to attend leaves of multiple scales. The leaf count from multiple resolutions is fused using a novel technique to get the final count. The second method is detection with a regression model that counts the leaves after locating leaf center points and aggregating them. The algorithms are evaluated on the Leaf Counting Challenge (LCC) dataset of the Computer Vision Problems in Plant Phenotyping (CVPPP) conference 2017, and a new larger dataset of banana leaves. Experimental results show that both methods outperform previous CVPPP LCC challenge winners, based on the challenge evaluation metrics, and place this study as the state of the art in leaf counting. The detection with regression method is found to be preferable for larger datasets when the center-dot annotation is available, and it also enables leaf center localization with a 0.94 average precision. When such annotations are not available, the multiple scale regression model is a good option.

플라즈마 방전수 및 바이오블록 처리를 달리한 로메인 상추의 생장변화 및 항산화 특성

This study intended to analyze growth changes and antioxidant activities of Romaine lettuce by different treatments of microbubble plasma discharge water and bioblock (far-infrared radiation). Hydroponics were performed in smart farm environments, and the used Romaine lettuce was the Caesar's Green variety. The treatment conditions of Romaine lettuce (TRL) are as follows: TRL-1 was treated with plasma discharge water and 30 bioblocks, and TRL-2 was treated with plasma discharge water and 40 bioblocks. On the other hand, TRL-3 was not treated. Romaine lettuce was grown for a total of 49 days and the growth changes were measured at every seven days. In growth changes, TRL-2 was significantly higher (p<0.05) than other treatments in germination rate and weight, leaf length, and leaf count. The total polyphenol and flavonoid contents were analyzed as similar levels between the samples, but TRL-2 showed higher antioxidant activities in ABTS radical scavenging (%) compared to the other samples. As a result of this study, TRL-2 conditions are evaluated to affect the growth and antioxidant functions of Romaine lettuce.

Monocots and Dicot Weeds Growth Phases Using Deep Convolutional Neural Network

The agriculture plays vital role in societies and requires research, planning and execution. In this research paper we have classified 20 types of weed species growth that grow in the Ravi season .It is impotent to identify and growth estimation of weed using deep learning technology in the field of convolution neural networks. This looks at situ images involving 20 weed species grown within a Time, 10500 leaves of these drawings were used for the trained of the weed statistics is taken from the rabi crop and its weeds are divided into 20 increase instruction. Image was an ordinary overall performance assessment of this proposed convolutional neural network using inceptionv3 model on 2000 pictures tested, which in addition are several in cropping, soil types, photograph judgments, and lighting fixtures situations. The common ordinary performance of this method met the maximum accuracy of 90.79% and the minimum accuracy of 25.68% for detecting polygon images. Minimum accuracy 8 leaf count leaf. The dataset consists of 1500 images identifying 6 weed households with 20 weed species, in overall 10500 annotations have been made. Three RGB virtual cameras have been used for photo capturing: Intel Real Sense LiDAR digital camera L515, Canon digital SLR DIGICAM EOS 850 D 18-55IS STM, and SONY w800. Pictures had been grown on food crops and weeds in a managed environment and taken at discipline conditions at specific increase degrees the ones consequences propose that The CNN and its associated models can process a lot of weed pictures simultaneously.

Characterization and Morphological Typology Of Fig Variety (Ficus carica) In The Tlemcen Region

The purpose of this study is to describe morpho-biometric and identify fig varieties in Tlemcen region, based on 33 morphological markers (24 qualitative and 9 quantitative traits). The samples collected are processed by the ImageJ software for taking measurements. The statistical analysis was carried out using the SAS version 9 software. The results of the descriptive analysis of morphological traits studied allowed us to describe each variety and estimated the variability associated with each character of the different varieties sampled. The results of the principal Component Analysis (PCA) allowed the distinction of nine groups. Comparison of the data collected for each two-to-two variety and the estimate of the square distance between them showed that there is a highly significant difference between all local varieties. Then the comparison between local varieties and imports allowed the population to be separated into 16 distinct groups. Statistical analysis identified skin color as the primary discriminating factor between local varieties followed by fruit length, apical branching and leaf count per shoot. Statistical analysis of qualitative and quantitative morphological data from the 33 characters in study is to be effective for the distinction of new local varieties. That said, morpho-biometric description and local variety identification must be verified by the molecular tool to embark on an effective program of management and genetic improvement of this important biological resource.

Characterization and Morphological Typology Of Fig Variety (Ficus carica) In The Tlemcen Region

The purpose of this study is to describe morpho-biometric and identify fig varieties in Tlemcen region, based on 33 morphological markers (24 qualitative and 9 quantitative traits). The samples collected are processed by the ImageJ software for taking measurements. The statistical analysis was carried out using the SAS version 9 software. The results of the descriptive analysis of morphological traits studied allowed us to describe each variety and estimated the variability associated with each character of the different varieties sampled. The results of the principal Component Analysis (PCA) allowed the distinction of nine groups. Comparison of the data collected for each two-to-two variety and the estimate of the square distance between them showed that there is a highly significant difference between all local varieties. Then the comparison between local varieties and imports allowed the population to be separated into 16 distinct groups. Statistical analysis identified skin color as the primary discriminating factor between local varieties followed by fruit length, apical branching and leaf count per shoot. Statistical analysis of qualitative and quantitative morphological data from the 33 characters in study is to be effective for the distinction of new local varieties. That said, morpho-biometric description and local variety identification must be verified by the molecular tool to embark on an effective program of management and genetic improvement of this important biological resource.

Dynamics of Maize Vegetative Growth and Drought Adaptability Using Image-Based Phenotyping Under Controlled Conditions.

Changes in climate are likely to have a negative impact on water availability and soil fertility in many maize-growing agricultural areas. The development of high-throughput phenotyping platforms provides a new prospect for dissecting the dynamic complex plant traits such as abiotic stress tolerance into simple components. The growth phenotypes of 20 maize (Zea mays L.) inbred lines were monitored in a non-invasive way under control, nitrogen, and water limitation as well as under combined nitrogen and water stress using an automated phenotyping system in greenhouse conditions. Thirteen biomass-related and morphophysiological traits were extracted from RGB images acquired at 33 time points covering developmental stages from leaf count 5 at the first imaging date to leaf count 10–13 at the final harvest. For these traits, genetic differences were identified and dynamic developmental trends during different maize growth stages were analyzed. The difference between control and water stress was detectable 3–10 days after the beginning of stress depending on the genotype, while the effect of limited nitrogen supply only induced subtle phenotypic effects. Phenotypic traits showed different response dynamics as well as multiple and changing interaction patterns with stress progression. The estimated biovolume, leaf area index, and color ratios were found to be stress-responsive at different stages of drought stress progression and thereby represent valuable reference indicators in the selection of drought-adaptive genotypes. Furthermore, genotypes could be grouped according to two typical growth dynamic patterns in water stress treatments by c-means clustering analysis. Inbred lines with high drought adaptability across time and development were identified and could serve as a basis for designing novel genotypes with desired, stage-specific growth phenotypes under water stress through pyramiding. Drought recovery potential may play an equal role as drought tolerance in plant drought adaptation.

Effect of selected substrates and chitosan on growth performance of orchid tissue culture seedling under net house

Orchids are considered as the most beautiful and significant cut blossom and pruned plants throughout the world. Therefore tissue culture technique was used as one of the alternative to mass propagate the orchids to fulfil the demand. This study was conducted to find out the effect of chitosan and different substrates on growth performance of Dendrobium Shavin White orchid seedling from tissue culture environment to net house. The tissue cultured plants went through prehardening stage to reduce mortality after the switch to ex vitro conditions. Five (5) treatments consist of combination substrates were tested which were T1: rice husk + chitosan, T2: peat moss + chitosan, T3: EFB + chitosan, T4: rice husk + peat moss + chitosan and T5: rice husk + peat moss + EFB + chitosan whereas charcoal as a control (T0). The result showed that the highest plant height and leaf length which did not significantly different were T3 (6.20cm plant height, 4.83cm leaf length), and T5 (6.55cm plant height, 4.51cm leaf length). Meanwhile, T3 was an effective for number of leaf production with 4.14 leaf count. Hence, this data will be used to acclimatize tissue cultured of Dendrobium Shavin White orchid for commercial used.

Growth, yield, and postharvest quality of cabbage (Brassica oleracea L.) in response to different levels of nitrogen fertilizer

In the Philippines, vegetable farmers generally apply excessive amounts of fertilizer to maximize yield while not understanding the cost impacts and environmental effects. This study aimed to evaluate the effects of different rates of nitrogen fertilizer on the productivity of two cabbage cultivars. The experiment was conducted as a factorial using a randomized complete block design in two growing seasons. The two factors included level of nitrogen (21, 141, 191, 241, 291, 341 and 391 kg N ha-1) and cultivar ('Resist Crown' and 'KEX-734') with each treatment combination replicated four times. The study showed that application of 291 kg N ha-1 resulted in the highest dry matter, largest heads (polar and equatorial) and highest average yield (35.49 t ha-1) in the two growing seasons. At 391 kg N ha-1, cabbage yielded the highest number of leaves and leaf count increased concomitantly with increasing N application rate. Results further showed that N application at the highest rate (391 kg N ha-1) resulted in the highest postharvest weight loss (33.7%) and shrivelling of cabbage heads. The cultivar 'Resist Crown' produced a greater number of leaves, largest polar head size and consequently highest mean yield (25.7 t ha-1) than cultivar 'KEX-734' in both cropping seasons. The results indicate that an application rate of about 291 kg N ha-1 is recommended for cabbage production since it increases yield through enhancing the size and weight of the cabbage head. 'Resist Crown' was a better cultivar than 'KEX734' having greater yield and postharvest quality. © 2021 International Society for Horticultural Science. All rights reserved.

Stimulation effect of synthetic plant growth regulator (GA3 and BAP) on young cinchona plant (Cinchona ledgeriana) grown in lowland

Cinchona is a pharmaceutical plant that produces medical substance. This plant contains copious type of alkaloid such as quinine, quinidine, cinchonine, and cinchonidine. Several obstacles might restrict cinchona cultivation, mainly climatic factors such as temperature and rainfall. One of the efforts to improve cinchona growth in lowland through the application of synthetic plant growth regulator (PGR). Two stages of experimental design were used in this study: first stage was the application of GA3 in six levels from October to December 2017 and the second stage was six levels of BAP concentrations treatment from July until October 2018. The experimental design used was randomized block design (RBD) with 4 replications at low altitude area for cinchona plant. The results showed GA3 applications have a significant effect towards leaves width and plant height. The 225 ppm of GA3 dosage produced maximum leaf width, 100ppm yielded the maximum number in plant height, and 150ppm produced the highest leaf count. While GA3 as a PGR improved the vegetative characters, various concentration of BAP had positive impact for stem diameter, leaves number, leaves area, and chlorophyll content on young cinchona plant grown in lowland area.

Bayesian hybrid analytics for uncertainty analysis and real‐time crop management

AbstractDynamic, deterministic agricultural models, and current machine learning technologies based on sensor data, enable and support decision making for on‐farm management. However, their predictions are subject to various sources of uncertainty. Hybrid analytics that leverage both modelled and sensor data provide predictive information that makes the best of both approaches in a timely fashion to inform operational decision making and enable inclusive uncertainty quantification. We describe and evaluate a probabilistic Bayesian data assimilation tool that combines the state variables from the Sirius wheat (Triticum aestivum L.) development model with time‐series environmental and leaf count data. Additionally, the uncertainty associated with input parameters is quantified via expert opinion. The Bayesian approach obtained point estimates through time that were accompanied by inclusive, probabilistic, 95% credible intervals. At the end of simulation, a typical model predicted a final leaf number of 6.6 leaves, Sirius alone predicted seven leaves and the mean of the observed data was 6.7 leaves. The 95% credible interval was estimated as 5.1–8.4 leaves. Importantly, the tool was able to “redirect” simulated outputs if input parameters such as minimum leaf number or base phyllochron were incorrectly specified, with the implication that on‐farm decision makers would have advance warning of variation in expected harvest date. Relatively few plants with time‐intensive data were sufficient to fit the model, however, more plants would be desirable to reduce the rather wide range of credible intervals. Nevertheless, the tool shows potential and could be readily implemented with low resource requirements, providing more finely tuned harvest date information, with probabilistic uncertainty quantification built‐in, for on‐farm decisions.

Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping

Precision management of agricultural fields as well as plant breeding are central factors for keeping yields high and to provide food, feed, and fiber for our society. A key element in breeding trials but also for targeted management actions is to analyze the growth state of individual plants objectively and at a large scale. In this letter, we address the problem of analyzing crops in real agricultural fields based on camera data recorded with mobile robots and to derive information about the plant development, e.g., to monitor phenotypic traits such as growth stage. We propose a novel single-stage object detection approach that localizes crops and weeds in the field. At the same time, it detects plant-specific leaf keypoints intending to estimate leaf count at a plant level, which is a key trait for classifying the growth stage. We implemented and thoroughly tested our approach on real sugar beet fields. As our experiments show, it performs the required detections and shows superior performance with respect to a state-of-the-art two-stage approach based on Mask R-CNN.

The Effect Of The Administration Of Calcium Chloride (CaCl2) And Phosphoric Acid Fertilizer (H3PO4) On The Growth And Production Of Strawberry (Fragaria ananassa)

Strawberry production in Indonesia has been on the rise in various regions, including Aceh, Riau, Jambi, DKI Jakarta, West Java, Central Java, East Java, and others. According to the Central Bureau of Statistics (2021), production fluctuated from 573.00 tons in 2019 to 559.00 tons in 2020, rebounding to 838.00 tons in 2021. Factors influencing production include land availability, proper fertilization methods, and planting media. Adequate fertilization, considering both dosage and frequency, plays a crucial role. The study, conducted at Balitjestro GreenHouse in Batu City, East Java, focused on the interaction between lime and fertilizer on strawberry plant growth and yield. Synthetic fertilizers, particularly phosphate fertilizers, are essential for plant growth. The research utilized a completely randomized design with two factors: CaCl2 lime (S) and phosphoric acid fertilizer (R), resulting in two treatment combinations (4x4). Each treatment, repeated three times, yielded 48 experimental units, with one plant per unit. Results indicated that the addition of CaCl2 lime and phosphoric acid fertilizer had a minimal impact on strawberry plant growth and production. Specific combinations, such as calcium chloride and phosphoric acid fertilizer with KCL 7.2 grams and urea 12.5 grams, did not significantly affect vegetative growth parameters like plant height and leaf count. However, these combinations did influence strawberry plant production. The study underscores the need for careful nutrient management to optimize strawberry production in Indonesia.

Effect of altered salinities on the growth and root performance in Ceriops tagal seedlings

Mangrove forests degradation caused by anthropogenic activities leads to a decrease in the composition of mangrove forests. Mangrove forest rehabilitation activities, therefore, are needed to improve mangrove forest ecosystems. Research on the growth and development of true mangrove seedling roots of Ceriops tagal on various salinity was examined in a greenhouse, Faculty of Agriculture, Universitas Sumatra Utara. This study used a non-factorial Complete Randomized Design with 5 treatments salinity (0%, 0.5%, 1%, 2%, and 3% concentrations) and repeated with ten replications. Results showed that 0.5% salinity concentration dominated the observed parameters, such as seedling growth height, seedling diameter, number of leaf count, leaf area, leaf thickness, root diameter, canopy water content 0.5%, and root and shoot ratio. By contrast, the highest shoot water content was at 0% salinity and root length at 2.0% salt concentration. This study indicated that the best growth of C. tagal seedlings was at 0.5% salinity concentration because more than 80% of parameter measurements were at 0.5% salinity concentration.

Genetic parameters and selection indexes for biofortified red leaf lettuce

Abstract: The objective of this work was to determine the genetic parameters and the efficiency of different selection indices for biofortified red leaf lettuce (Lactuca sativa var. crispa) lines with agronomic and nutritional potential. The experiment was carried out in a randomized complete block design with three replicates and 31 crisp-textured and red-tinted leaf lettuce genotypes: the Belíssima cultivar, with a low carotenoid content and rich in anthocyanins; and 30 lines from the cross between the Belíssima and Uberlândia 10000 cultivars, rich in carotenoids and with a low anthocyanin content. The assessed traits were: total green mass (g), stem diameter (cm), leaf count, plant diameter (cm), foliar temperature (°C), soil plant analysis development (SPAD) index, anthocyanin content (mg 100 g-1 sample), and bolting (days after sowing). To estimate selection gains, 12 genotypes were selected through selection indices. The traditional index proposed by Smith & Hazel and the sum of ranks index by Mulamba & Mock provided the highest selection gains in the biofortified lettuce. The coefficient of genotypic determination for leaf count, anthocyanin content, bolting, and SPAD index is of high magnitude.

Greater biodiversity in regenerated native tropical dry evergreen forest compared to non-native Acacia regeneration in Southeastern India

The 20 km-50 km-wide belt of tropical dry evergreen forest inland from the southeastern coastline of India has undergone biodiversity loss due to timber harvest and agriculture in the last 200 years. Reforestation restores ecosystem function and increases population sizes and diversity. Sadhana Forest reforested an area of 28 ha and replenished the water table through intensive soil moisture conservation. Results show rapid growth of planted native species and germination of two species of dormant Acacia seeds. Using standardised inventory methods, we documented 75 bird, eight mammal, 12 reptile, five amphibian, 55 invertebrate species, and 22 invertebrate orders. Bird abundance at point count stations, invertebrate sweep net captures and leaf count detections, and Odonate and Lepidopteran visual detections along fixed-paced transects were significantly greater in areas with native plants. Sadhana Forest's reforestation demonstrates the potential to restore ecosystems and replenish water tables, vital components to reversing ecosystem degradation, and corroborates reforestation efforts in other regions of the world.