Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence

The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.

Simple Culture Techniques for Germinationpromoting Fungi and Armillariella mellea in Artificial Planting of Gastrodia elata

This study aimed to explore simple culture techniques for symbiotic germination-promoting fungi and Armillariella mellea in artificial planting of Gastrodia elata, i.e., how to use a corner of ordinary house as inoculation room and cultivating site, how to use a pressure cooker for cooking food as sterilization tool, and how to use ordinary household heating as heating measures.

Effects of Planting Density and Row Spacing on Grass Yield of Forage Sweet Sorghum(Sorghum bicolor [L.]Moench)

[Objective] The aim was to explore high-yielding cultivation techniques for forage sweet sorghum. [Method[ The effects of planting density and row spacing on plant productivity and grass yield of forage sweet sorghum （Sorghum bicolor （L.） Moench） were compared using split-plot design and LSD method of IBMSPSSStatis- ticsv22. [Result]The planting density and row spacing had important influence on the plant productivity and yield of forage sweet sorghum. The optimum planting density- row spacing combination for plant productivity of forage sweet sorghum was A1B,, i. e., planting density of 75 000 plants/hm2 and row spacing of 40 cm, and the opti- mum combination for yield of forage sweet sorghum was A2B,, i.e., planting density of 225 000 plants/hm2 and row spacing of 40 cm. [Conclusion] This study will pro- vide theoretical basis and technical support for the production practice of forage sweet sorghum.

Mulch Grasses Planting and Management Techniques in Orchards of Southern China Based on Different Utilization Purposes

Planting mulch grasses in orchards,as a technique to build ecological orchards,can be one of the strategic approaches for rural revitalization.This study sorted out the common varieties of mulch grasses and analyzed their application statuses in orchards of southern China.According to different utilization purposes of mulch grasses in orchards,scientific suggestions were given from aspects of grass selection,cultivation techniques,management methods and use modes.The study will provide reference for the construction of ecological orchards in southern China.

Preparation and Water Retention Properties of Clay-based Sand-fixing and Grass-planting Materials

Targeting the problem of available water conservation in sand fixation, the sand-fixing and grass-planting materials were prepared with clay modified by emulsifying vegetable waxes and octylphenol polyoxyethylene ether （OP4）. The water retention property was studied in simulating desertification environmental climate and the materials were characterized by means of UV-Vis, SEM, FTIR, XRD and TGA measurements. The experimental result showed that the materials had excellent water retention properties, due to that vegetable waxes adhered evenly to clay particle surfaces, made the clay pores changing from hydrophilic to hydrophobic and so inhibited the water evaporation. Grass-planting experiment showed that, with reasonable mass ratio of clay, vegetable waxes and surfactant, the materials not only inhibited water evaporation but also maintained sound air permeability so shat the germination rate and survival rate of grass were significantly improved.

The Effect of Water and Salt Stress on Paspalum dilatatum,a Constituent of Pampas Natural Grasslands

The effects of the salt stress on plant growth are usually increased by the water stress.We studied the impact of both stresses in simultaneous pulses of drought and salinity on Paspalum dilatatum.This forage species is native to South America,spread in grasslands in many tropical,subtropical,and temperate areas of the world,and very common in grasslands of the Flooding Pampas of Argentina.Mimicking what happens in nature.We compared a pot experiment,a non-stressed control against water stress for a month(midpoint between field capacity and wilting point),and two saline stresses(moderate,6 d·Sm^(−1)and strong,12 d·Sm^(−1)),also for a month.Aerial biomass(green leaf;non-leaf green material,and dry material)and roots were harvested,weighed,and analyzed for nitrogen,phosphorus,and cations.The biomass of all components significantly decreased when both stresses were applied.Water plus strong saline stress reduced by half the total biomasses,compared to the control.The proportion of aerial biomass/root biomass ratio as well as aerial green component/dry materials ratio tend to decrease when subjected to both stresses.Nitrogen concentration in plants was not significantly affected,but phosphorus concentration increased in aerial biomass components,from 0.10 to 0.18 mg·kg^(−1)between the extreme treatments,but did not change in roots.Sodium concentration in plants increased(i.e.,in green leave sodium(Na)increased from 0.27 to 2.01 mg·kg^(−1)between the extreme treatments),whereas other cations either did not change or decreased,affecting the ratios between them.Sodium performance allows us to infer that the Na accumulation of P.dilatatum behaves in an intermediate range,compared to very tolerant to salts or non-salt tolerant species of the Paspalum genus.In agreement,when salts were applied in the form of a pulse,P.dilatatum tolerated higher salinity than that found by other authors for the same species,using continuous salinity.

Design of Control and Monitor System Applied in Artificial Grass Tufting Machine

Based on the control requirements of artificial grass tufting machine,an overall plan is proposed on control of CANopen bus.In the control system,on the basis of CoDeSys soft PLC technology of fieldbus control system,LMC20 realizes the communication by making CANopen bus connect with ATV71,OTB module and these underlying slave stations.The humanized operation interface is realized by touch screens and user-friendly application which is developed through CoDeSys,and detailed analysis of the monitor system and the principle and method of monitor system are also provided in the paper.

Physiological and Biochemical Response of Artificial Wetland Plant under Electric Field

By measuring wetland plants chlorophyll content,malondialdehyde(MDA) content and superoxide dismutase(SOD) enzyme activity,the changes of wetland plant physiological characeristics under different power strength were studied,and the mechanism of electric field on plant physiological characteristics was analyzed to provide a theoretical basis for the pollutant removal ability strengthening of artificial wetland under electricfield.The results showed that compared with the control plants,low-intensity-voltage(1 V and 3 V) had no significant effect on the normal physiological and biochemical indexes of the plants,and the growth trend was better than the control group;with the voltage increasing,plant chlorophyll content,MDA content and SOD activity were greatly affected,indicating that plants were under strong oxidative stress,and the growth was damaged.Therefore,a suitable electric field could enhance the sewage treatment effect of constructed wetland.

人工和天然草地对青年女性跳跃落地时下肢生物力学表现的影响

背景:研究发现运动者解剖结构、激素水平、神经肌肉功能等内部因素与前交叉韧带损伤风险密切相关,而运动场地材质等外部因素也成为影响非接触性前交叉韧带损伤发生的危险因素之一,但目前研究中对其关注度相对不足。目的:探讨人工草地和天然草地对青年女性跳跃落地时下肢生物力学表现影响。方法:根据测试需要将符合GB/T 20033.3-2006以及GB/T 19995.1-2005标准的人工草地和天然草地平整固定于2块三维测力台上。21名青年女性自愿受试者分别在人工草地和天然草地完成跳跃落地任务。受试者站立于台阶上随后向前跳跃,跳落至测力台后立即全力纵跳并再次落至测力台上,2次落地均要求双脚分别落至2块测力台上,整个跳跃动作流畅无停顿视为成功。同步采集下肢落地过程中运动学、动力学及肌电数据,比较分析二者差异。结果与结论:①动力学方面,在天然草地进行跳跃落地任务时初始触地时刻后向地面反作用力、纵向地面反作用力显著低于在人工草地时(P<0.05;P<0.01);峰值地面反作用力时刻二者亦显著低于在人工草地时(P<0.05;P<0.05),且表现出更高的膝关节屈曲力矩(P<0.01)。②肌电方面,在天然草地进行跳跃落地任务时初始触地时刻后100 ms内股内侧肌、股外侧肌、胫骨前肌肌电活动水平显著低于在人工草地时(P<0.05;P<0.01;P<0.05)。③结果说明,在人工草地进行跳跃落地任务时较天然草地时会出现使前交叉韧带张力升高的生物力学表现。

Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images

Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron （MLP） artificial neural network （ANN） based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density （μg cm^-2） in the 17 testing images was 8.41%.

Plant water use strategies in the Shapotou artificial sand-fixed vegetation of the southeastern margin of the Tengger Desert, northwestern China

Stable oxygen and hydrogen isotopic compositions(δ^(18)O and δD) of plant xylem water and its potential water sources can provide new information for studying water sources, competitive interactions and water use patterns of plants. The contributions of different water sources to three plants, Hedysarum scoparium(HS), Caragana Korshinskii(CK) and Artemisia ordosica(AO), were investigated in the artificial sand-fixed vegetation of Shapotou, the southeastern margin of the Tengger Desert of northwestern China, based on meteorological data and δ^(18)O and δD values of precipitation, groundwater, soil water and xylem water of HS, CK and AO. Our results indicated that soil water infiltration through precipitation was the main water source of the artificial sand-fixed vegetation. Obvious differences in soil water content and in δ^(18)O of soil water and xylem water were found among different seasons. No relationship was found between the δ^(18)O in plant xylem water and in soil water in January. The same water use patterns were found in CK, HS and AO in May, suggesting they have the same water sources. The different water sources of CK, HS and AO in August indicate that water competition occurred. In addition, the main water sources of CK, HS and AO in August mainly come from shallow soil water, while they use relatively deep soil water in May. This phenomenon is related to the differences of soil water content throughout soil profile, precipitation, transpiration and water competition under different growth periods. The water use patterns of CK, HS and AO respond to soil water content throughout the soil profile and their competition balance for water uptake during different growth season. The results indicate that these sandfixed plants have developed into a relatively stable stage and they are able to regulate their water use behavior as a response to the environmental conditions, which reinforces the effectiveness of plantation of native shrubs without irrigation in degraded areas.

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

Application Research of Water-saving and Drought-resistant Landscape Plants:A Case Study of Inner Mongolia Drought-resistant Mongolian Grasses

Construction of "water-saving landscape architecture" is a crucial content of building "conservation-minded society'',an important approach of ensuring the sustainable development of landscaping industry.It targets at exploring a reasonable means of using the nature,so as to improve ecological conditions and environment,save resources and energies,and promote the harmonious coexistence of man and nature.Landscape plant is a significant component of landscape architecture,it is a key section to choose proper drought-resistant plant species for the landscape construction.

Estimation of Copper and Molybdenum Grades and Recoveries in the Industrial Flotation Plant Using the Artificial Neural Network

In this paper, prediction of copper and molybdenum grades and their recoveries of an industrial flotation plant are investigated using the Artificial Neural Networks (ANN) model. Process modeling has done based on 92 datasets collected at different operational conditions and feed characteristics. The prominent parameters investigated in this network were pH, collector, frother and F-Oil concentration, size percentage of feed passing 75 microns, moisture content in feed, solid percentage, and grade of copper, molybdenum, and iron in feed. A multilayer perceptron neural network, with 10:10:10:4 structure (two hidden layers), was used to estimate metallurgical performance. To obtain the optimal hidden layers and nodes in a layer, a trial and error procedure was done. In training and testing phases, it achieved quite correlations of 0.98 and 0.93 for Copper grade, of 0.99 and 0.92 for Copper recovery, of 0.99 and 0.92 for Molybdenum grade and of 0.99 and 0.94 for Molybdenum recovery prediction, respectively. The proposed neural network model can be applied to determine the most beneficial operational conditions for the expected Copper and Molybdenum grades and their recovery in final concentration of the industrial copper flotation process.

Detection of Subsurface Cavities in a Power Plant Through Artificial Neural Network from Micro-Gravity Data

Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam construction.In order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered for construction of a power plant site in Iran.First we gain the residual anomalies through bouger anomalies and then we design an Artificial Neural Network(ANN)which is trained by a set of training data.The ANN was tested for both synthetic and real data.For real data some suitable features are derivate from residual anomalies and applied to

Simulation of Low TDS and Biological Units of Fajr Industrial Wastewater Treatment Plant Using Artificial Neural Network and Principal Component Analysis Hybrid Method

Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of par- ticular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the treatment plant was divided into two main subsystems including: Low TDS (Total Dissolved Solids) treatment unit and Biological unit (extended aeration). The multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant, located in Mahshahr—Iran that qualita- tive and quantitative characteristics of its units were used for training, calibration and validation of the neural model. Also, Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. The results of L-TDS unit showed good accuracy of the models in estimating qualitative profile of wastewater but results of biological unit did not have sufficient accuracy to being used. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of outputs.

Relationship between Plant Species Diversity and Plant Biomass of Orchard Grass and Lucerne Sown in Different Ratios in the Province of Salamanca, Spain

A field experiment was carried out at the CSIC Mu&#241ovela farm belonging to the Spanish National Research Council (CSIC) in order to evaluate the effect of sowing orchard grass (Dactylis glomerata var. Trerano) and lucerne (Medicago sativa var. Aragon) in monoculture and in combination. The experiment was based on a randomized block designed with a factorial arrangement (5 × 2). Experimental units were 40 plots distributed in four blocks. The phosphorus fertilization (P) factor included two types of conditions: basal fertilization without phosphorus (-P) and basal fertilization with phosphorus (+P), and the vegetation cover factor (T) included five conditions depending on the grass (G) and the legume (L). Above-ground biomass showed statistically significant differences among seasons and years (P Lolium perenne L. and Poa pratensis L. throughout the three years indicated that both species significantly increased their presence over time regardless of the treatments applied. The analysis performed for the other plant species (those other than grasses and legumes) allowed us to determine that the T1 and T5 treatments, which correspond to single species not treated with the application of phosphorus, influenced the presence of 70% of other species planted. Our specific aim was to explore how changing plant biotic diversity affects productivity under a given set of conditions. We manipulated plant species richness as an experimental factor to determine if productivity would be affected by changes in the ratios of plants sown.

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop productivity.Some of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes.With this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and classification.The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner.In addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions.The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the features.Finally,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for classification.In order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was conducted.The results were assessed under different measures.The obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

Impacts of Sward Renewal Method with Perennial Ryegrass (Lolium perenne) on Dry Matter Yield, Tiller Density and Nitrate Leaching

The objective of this study was to evaluate alternative methods of grassland renewal (reseeding) with perennial ryegrass and quantify their effects on subsequent DM yield, tiller density and nitrate leaching. Two experiments were carried out;the first focused on quantifying the influence of Autumn reseeding (August cultivation), and the second, on Spring reseeding (May cultivation) on sward establishment and grass DM production. The study incorporated six treatments namely: direct drill (DD), disc plus power harrow (DPH), power harrow (PH), conventional-plough, till and sow (PLO), and the chemical application of diquat to suppress the existing sward followed by direct drilling (DIQ), represented a rejuvenation method as opposed to full renewal (Spring trial only). All treatments were compared against a control (old permanent pasture). Reseeded swards produced more seasonal (P < 0.05) and total (P < 0.01 Autumn only) DM yield than the control sward. All reseeding methods increased the perennial ryegrass tiller density of the sward compared to the old permanent pasture (P < 0.05 Autumn trial, P < 0.001 Spring trial). All sward renewal methods evaluated were equally as effective as the conventional method of grassland reseeding with the DIQ rejuvenation method intermediate as measured in terms of DM yield and PRG tiller density. The results of the study show no significant difference in the level of nitrate lost in leachate following reseeding regardless of method used or indeed any difference between reseeded swards and old permanent pasture.

Application of FAHP and Artificial Neural Network on Clothing Plant Location

Clothing manufacturers＇ direct investment and joint ventures in developing regions have seen to grow rapidly in the past few decades. Non-optimized selection can contribute to adverse effects affecting the performance of the plants on aspects of productivity, manufacturing and logistics cost. Selection of proper plant location is thus crucial. The conventional approaches to sites location are based on the factors and their weights. However, determining the weight of each factor is very difficult and time consuming. While the situation is changed, all the work must be redone again. This study aims to develop a decision-making system on clothing plant location for Hoog Kong clothing manufacturer. The proposed system utilizes artificial neural network to study the relationship between the factors and the suitability index of candidate sites. Firstly, the factors are stratified using the fuzzy analytical hierarchy process （FAHP） by review the related references and interviewing the experts. Secondly, the corresponding data are collected from the experts by questionnaire and the related government publication. Finally, the feedforward neural network with error backpropagation（EBP） learning algorithm is trained and applied to make decision. The results show that the proposed system performs well and has the characteristic of adaptability and plasticity.