Free proline accumulation in rice plants under different leaf water potential

Seedlings of drought-tolerance rice varieties Han 501and Han A03,and the drought sensitive varietiesNanjing 11 and Yanjing 2 were raised in a paddyfield and transplanted into pots at the age of 8leaves.Water stress started at the tillering stage byholding water from 0 MPa of the soil water potentialin pots till the leaves showed seriously wilting.

Quantity of plant leaf area on three major public squares in Kunming City, China

Regressive formulae to calculate the quantity of plant leaf area for 13 species of ornamental plants were set up based on investigation data of 30 species on 3 major public squares (Dongfeng square, Shengli square and Guandu square) in Kun-ming City, China, which were applied to calculate quantities of plant leaf area of these 13 species. The quantities of plant leaf area for the other 17 ornamental plant species on these squares were directly measured, and the total quantity of plant leaf area of each studied square was obtained individually. The results showed that the quantity of plant leaf area on Shengli square with ornamental plants structure composed of arbor tree species, shrub tree species and turf grass was highest among the three squares. It is believed that the design model of multi-storied vertical structure and proper tending of plant community could not only increase the quantity of plant leaf area, but also play an important role in generating ecological and landscaping benefits. Some corresponding suggestions were put forward on the basis of comprehensive analyses on the plant leaf area quantity of the three representative squares in Kunming urban area.

Mango Leaf Disease Identification Using Fully Resolution Convolutional Network

Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms.Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant leaf.To solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango leaf.The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques.We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,Pakistan.To evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and Unet.Furthermore,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other models.We have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’identification.Accordingly,an automated approach helps pathologists and mango growers detect and identify those diseases.

Combining Phytate/Ca^(2+) Fractionation with Trichloroacetic Acid/Acetone Precipitation Improved Separation of Low-Abundant Proteins of Wheat (Triticum aestivum L.) Leaf for Proteomic Analysis

Proteomic assessment of low-abundance leaf proteins is hindered by the large quantity of ribulose-1,5-bisphosphate carboxylase/oxygenase （Rubisco） present within plant leaf tissues. In the present study, total proteins were extracted from wheat （Triticum aestivum L.） leaves by a conventional trichloroacetic acid （TCA）/acetone method and a protocol first developed in this work. Phytate/Ca2＋ fractionation and TCA/acetone precipitation were combined to design an improved TCA/acetone method. The extracted proteins were analysed by two-dimensional gel electrophoresis （2-DE）. The resulting 2-DE images were compared to reveal major differences. The results showed that large quantities of Rubisco were deleted from wheat leaf proteins prepared by the improved method. As many as （758±4） protein spots were detected from 2-DE images of protein extracts obtained by the improved method, 130 more than those detected by the TCA/acetone method. Further analysis indicated that more protein spots could be detected at regions of pI 4.00-4.99 and 6.50-7.00 in the improved method-based 2-DE images. Our findings indicated that the improved method is an efficient protein preparation protocol for separating low-abundance proteins in wheat leaf tissues by 2-DE analysis. The proposed protocol is simple, fast, inexpensive and also applicable to protein preparations of other plants.

Kinetics of low level chemiluminescence from plant leaf smoked by air pollutants

The kinetics of low level chemiluminescence from Chinese white polpar leaf smoked by CO and two gaseous mixture of SO 2 and CO or SO 2, NO x and CO, and their luminescence intensity formula were described. The comparison of the results indicated that three kinds of the gaseous compounds could cause no changes of the substantial nature of foliar biophoton emission. However, they made the luminescence intensity, including I o (1) and I o (2) , altered in a certain degree, and the changes caused by the fumigation of CO and the mixed gas of SO 2 and CO were different from that made by the gaseous mixture of SO 2, NO x and CO in τ′ and τ″ of the photo induced luminescence from plant leaf.

Determination of volumetric elastic moduli of plant leaf cells based on pressure-volume curves

Volumetric elastic modulus （VEM） is an important parameter in biophysics and biomechanics of plants for in particular understanding cell growth. This paper proposes a new relation that can be used for precisely determining VEM. With the aid of this relation, it shows that the exponential approximation of the pressure-volume relationship adopted in most of the literatures in this field may lead to serious errors on VEM.

A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

Mechanical Characteristics of Typical Plant Leaves

Plant leaf is a natural composite biomaterial, and its strength is closely related to the microstructure. In this paper, themechanical characteristics of eight species of plant leaves were investigated and analyzed. The ultimate strength of leaves andthe hardness of leaf surfaces were measured by using universal testing machine and nanoindenter tester, respectively. The tensilestrength of the parallel microstructure was investigated based on its cross-sectional mechanical model. The results of tensiontests indicate that the ultimate strength of a leaf is related to the material composition and structure. The coriaceous leavesusually exhibit higher tensile strength. For example, the Phyllostachys pubescens leaf can achieve the maximum ultimatestrength of 5.9091 N·mm. It is concluded from the results of hardness tests that material components of leaf surface caninfluence the surface hardness evidently. The leaf surface composed of more lignin and cellulose materials shows a highersurface hardness than that composed of more carbohydrates materials.

SVM and KNN Based CNN Architectures for Plant Classification

Automatic plant classification through plant leaf is a classical problem in Computer Vision.Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like.Many efforts are made to automate plant classification using plant leaf,plant flower,bark,or stem.After much effort,it has been proven that leaf is the most reliable source for plant classification.But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations,like sizes,textures,shapes,and venation.Therefore,it is required to normalize all plant leaves into the same size to get better performance.Convolutional Neural Networks(CNN)provides a fair amount of accuracy when leaves are classified using this approach.But the performance can be improved by classifying using the traditional approach after applying CNN.In this paper,two approaches,namely CNN+Support Vector Machine(SVM)and CNN+K-Nearest Neighbors(kNN)used on 3 datasets,namely LeafSnap dataset,Flavia Dataset,and MalayaKew Dataset.The datasets are augmented to take care all the possibilities.The assessments and correlations of the predetermined feature extractor models are given.CNN+kNN managed to reach maximum accuracy of 99.5%,97.4%,and 80.04%,respectively,in the three datasets.

The application of near-infrared spectra micro-image in the imaging analysis of biology samples

In this research,suitable imaging methods were used for acquiring single compound images ofbiology samples of chicken pectorales tissue section,tobacco dry leaf,fresh leaf and plantglandular hair,respectively.The adverse effects caused by the high water content and thethermal effect of near infrared(NIR)light were effectively solved during the experiment procedures and the data procesing.PCA algorithm was applied to the NIR micro-image of chickenpectorales tissue.Comparing the loading vector of PC3 with the NIR spectrum of dry albumen,the information of PC3 was confimmed to be provided mainly by protein,i.e.,the 3rd score imagerepresents the distribution trend of protein mainly.PCA algorithm was applied to the NIR micro-image of tobacco dry leaf.The information of PC2 was confimed to be provided by carbohydrateindluding starch mainly.Compared to the 2nd score image of tobacco dry leaf,the comparedcorelation image with the reference spectrum of starch had the same distribution trend as the 2nd score image.The comparative correla tion images with the reference spectra of protein,glucose,fructose and the total plant alkaloid were acquired to confirm the distribution trend ofthese compounds in tobacco dry leaf respectively.Comparative correlation images of fresh leafwith the reference spectra of protein,starch,fructose,ghucose and water were acquired to confim the distribution trend of these compounds in fresh leaf.Chemimap imaging of plant glandularhair was acquired to show the tubular structure clearly.

Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

Since the beginning of time,humans have relied on plants for food,energy,and medicine.Plants are recognized by leaf,flower,or fruit and linked to their suitable cluster.Classification methods are used to extract and select traits that are helpful in identifying a plant.In plant leaf image categorization,each plant is assigned a label according to its classification.The purpose of classifying plant leaf images is to enable farmers to recognize plants,leading to the management of plants in several aspects.This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes.This modified algorithm works on different sets of plant leaves.The proposed algorithm examines several benchmark functions with adequate performance.On ten plant leaf images,this classification method was validated.The proposed model calculates precision,recall,F-measurement,and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms.Based on experimental data,it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.

Herbaceous Cultivation Test of Mulberry in Sichuan Sericultural Area

With Guiteyou 2 as the test mulberry variety, mulberry herbaceous cultivation field was established at the planting densities of 60 000,90 000 and 120 000 plants/hm2, respectively. Hybrid mulberry herbaceous cultivation test was conducted, with Tongxiangqing mulberry field of mid-fist-form at conventional planting density of 4 995 plants/hm2 as the control. The results showed that the leaf yield of herbaceous cultivation mulberry was closely related to the planting density. With the increasing planting density, the leaf yield per plant was decreasing, but the leaf yield per unit area of mulberry field was increasing. The annual leaf yield per unit area in the mulberry field with the average planting density of 120 000 plants/hm2 was 37 560 kg/hm2, 2.14 times of that of the control field. The moisture content of mulberry leaf in herbaceous cultivation field was 4.74% higher than the control; the contents of crude protein, crude fat, crude fiber and crude ash were 1.264%, 0.014%, 0.744% and 0.002%lower than the control, respectively. Comparison of leaf rearing with herbaceous cultivation mulberry and control mulberry showed that there was no significant difference in growth status of silkworm. The cocoon weight, cocoon shell weight, cocoon yield per 10 000 larvae, cocoon filament length and non-broken filament length of Chuanshan × Shushui were 12.81%, 14.29%, 13.85%, 5.95% and 7.68% lower than the control, respectively; but the percentage of common cocoons was 0.16% higher than the control; the cleanness and neatness were 0.2 and 1.25 points higher than the control;the filament size was 0.196 dtex lower than the control. The cocoon weight, cocoon shell weight, cocoon yield per 10 000 larvae, cocoon filament length and non-broken filament length of Jingsong × Haoyue were 11.06%, 10.20%, 11.53%, 9.46% and 9.03% lower than the control, respectively;but the percentage of common cocoons was 1.77% higher than the control; the cleanness and neatness were 0.12 and 2.50 points higher the control;and the filament size was 0.196 dtex lower than the control.

Language of plants: Where is the word?

Plants emit biogenic volatile organic compounds（BVOCs） causing transcriptomic, metabolomic and behavioral responses in receiver organisms. Volatiles involved in such responses are often called ＂plant language＂. Arthropods having sensitive chemoreceptors can recognize language released by plants. Insect herbivores, pollinators and natural enemies respond to composition of volatiles from plants with specialized receptors responding to different types of compounds. In contrast, the mechanism of how plants＂hear＂ volatiles has remained obscured. In a plant-plant communication, several individually emitted compounds are known to prime defense response in receiver plants with a specific manner according to the chemical structure of each volatile compound. Further, composition and ratio of volatile compounds in the plant-released plume is important in plantinsect and plant-plant interactions mediated by plant volatiles. Studies on volatile-mediated plant-plant signaling indicate that the signaling distances are rather short, usually not longer than one meter. Volatile communication from plants to insects such as pollinators could be across distances of hundreds of meters. As many of the herbivore induced VOCs have rather short atmospheric life times, we suggest that in long-distant communications with plant volatiles,reaction products in the original emitted compounds may have additional information value of the distance to emission source together with the original plant-emitted compounds.

Comparison of CNN-based deep learning architectures for rice diseases classification

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice diseases.Moreover,most CNN-based rice disease detection studies only considered a small number of diseases in their experiments.Both these shortcomings were addressed in this study.In this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in Bangladesh.In addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble techniques.The results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf diseases.The high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural systems.This research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

Thermo-sensitive Porous Polymer Membrane-immobilized Cellulose as a Switchable Enzyme Reactor for Tuning Its Enzymolysis via Variation Temperature

Immobilization of enzymes onto porous membranes has attracted considerable attention in recent years.However,enhancing the enzymolysis efficiency of the resulting enzyme reactors by varying the environmental conditions poses a great challenge.In this work,poly(styrene-maleic anhydride-N,N-dimethylacrylamide)was prepared and utilized to construct a thermo-sensitive porous polymer membrane-based enzyme reactor(TS-PPMER)after cellulase was immobilized onto the support by covalent bonding.The catalytic activity of the nano-reactor was evaluated by measuring the yield of the product,glucose,at different temperatures with carboxymethylcellulose as the substrate.Interestingly,the polymer chains coiled and formed numerous nano-pores at a high temperature,which induced the confine effect and greatly boosted the enzymolysis efficiency of TS-PPMER.Furthermore,the proposed TS-PPMER was applied in the hydrolysis of green plant leaves in Epipremnum aureum.This work shows great potential in obtaining biological resources by an environmentally friendly approach using smart polymer-based nano-reactors.

Linking species performance to community structure as affected by UV-B radiation:an attenuation experiment

Aims UV-B radiation is known to affect plant physiology and growth rate in ways that can influence community species composition and structure.Nevertheless,comparatively little is known about how UV-B radiation induced changes in the performance of individual species cascades to affect overall community properties.Because foliage leaves are primarily responsible for photosynthesis and carbon gain and are the major organ that senses and responds to UV-B radiation,we hypothesized that,under reduced UV-B radia-tion,species with larger leaf areas per plant would manifest higher growth rates and hence tend to improve their community status compared to species with smaller leaf areas per plant in herba-ceous plant communities.Methods We tested this hypothesis by examining plant traits(leaf area per plant and plant height),plant growth rate(aboveground biomass per plant and plant biomass per area)and community status(spe-cies within-community relative biomass)for 19 common species in a two-year field experiment in an alpine meadow on Tibetan Plateau.Important findings Aboveground biomass per plant,as well as per area,progressively increased in a 39%reduced(relative to ambient)UV-B treatment dur-ing the experimental period.At the second year,11 out of 19 species significantly or marginally significantly increased their plant height,leaf area per plant and aboveground biomass per plant.No species was negatively affected by reducing UV-B.As hypothesized,the increase in aboveground biomass per plant increased with increasing leaf area per plant,as indicated by cross-species regression analysis.Moreover,the change in species within-community status increased with increasing leaf area per plant.Our study demonstrates that UV-B radiation has differential effects on plant growth rate across species and hence significantly affects species composition and plant commu-nity structure.We suggest that UV-B radiation is an ecological factor structuring plant communities particularly in alpine and polar areas.