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 par...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.展开更多
Leaf area index (LAI) of natural vegetation is recognized as the most important variable for measuring vegetation structure over large areas, and for relating it to energy and mass exchange, which has been successfull...Leaf area index (LAI) of natural vegetation is recognized as the most important variable for measuring vegetation structure over large areas, and for relating it to energy and mass exchange, which has been successfully estimated from satellite resolution sensors. In this paper, according to the statistical analysis based on a lot of forest plots, the mathematical models of LAI distribution patterns in the hydro thermal spaces for five coniferous forest types in China were established. For the cold temperate larch forests growing in the dry and cold climate, their LAI increases with the increasing of warm index and precipitation in the way of hyperbolic quadratic surface. For the cold temperate spruce fir forests and temperate Pinus tabulaeformis forests, their LAI is negatively related to the annual mean air temperature in the way of the natural exponential curve, in order to adapt to the water oppressed environments. For the subtropical Pinus massoniana forests and Cunninghamia lanceolata forests growing in the warm and moist climate, their LAI is related to the annual mean air temperature in the way of the parabolic quadratic curve.展开更多
This study aimed to demonstrate change in spatial correlation between Korean pine （Pinus koraiensis Sieb. et Zucc.） and three rare species, and change in spatial distribution of four species in response to a range o...This study aimed to demonstrate change in spatial correlation between Korean pine （Pinus koraiensis Sieb. et Zucc.） and three rare species, and change in spatial distribution of four species in response to a range of selective cutting intensities. We sampled three plots of mixed Korean pine and broad-leaf forest in Lushuihe Forestry Bureau of Jilin province, China. Plot 1, a control, was unlogged Korean pine broad-leaf forest. In plots 2 and 3, Korean pine was selectively cut at 15 and 30 % intensity, respectively, in the 1970s. Other species were rarely cut. We used point-pattern analysis to research the spatial distributions of four tree species and quantify spatial correlations between Korean pine and the other three species, Amur linden （Tilia amurensis Rupr.）, Manchurian ash （Fraxinus mandshurica Rupr.）, and Mongolian oak （Quercus mongolica Fisch.） in all three plots. The results of the study show that selective cutting at 15 % intensity did not significantly change either the species spatial patterns or the spatial correlation between Korean pine and broadleaf species. Selective cutting at 30 % intensity slightly affected the growth of Korean pine and valuable species in forest communities, and the effect was considered nondestructive and recoverable.展开更多
叶面积指数(Leaf Area Index, LAI)作为植被结构和生长状况的重要指标和生态参数,能够较好地反映植被的生长状况与分布情况,本文基于LAI反映城市绿色空间的分布状况,以乌鲁木齐市作为研究区,使用2016~2022年的Sentinel-2系列遥感数据反...叶面积指数(Leaf Area Index, LAI)作为植被结构和生长状况的重要指标和生态参数,能够较好地反映植被的生长状况与分布情况,本文基于LAI反映城市绿色空间的分布状况,以乌鲁木齐市作为研究区,使用2016~2022年的Sentinel-2系列遥感数据反演了乌鲁木齐市夏季LAI时间序列数据。引入景观指数分析乌鲁木齐市2016~2022年不同等级绿色空间的组分与结构变化特征;使用结合Sen斜率估计的M-K趋势检验分析了乌鲁木齐市夏季LAI的变化趋势;并通过地理探测器分析了不同驱动因子对LAI的驱动作用。结果表明:(1)建成区内部多为低密度植被区域覆盖,高密度植被覆盖区域集中于城市边缘;(2)乌鲁木齐市的绿色空间面积主要呈减少趋势。(3)影响乌鲁木齐市绿色空间的驱动因子主要为土地利用类型和降水量;(4)交互探测表明驱动因子交互作用影响力强于单个驱动因子。展开更多
In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to ...In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to administrate a hierarchical list of leaf images, some sorts of edge detection can be performed to identify the individual tokens of every image and the frame of the leaf can be got to differentiate the tree species. An approach based on back-propagation neuronal network is proposed and the programming language for the implementation is also Riven by using Java. The numerical simulations results have shown that the proposed leaf strategt is effective and feasible.展开更多
Large sample sampling was adopted in this paper for the statistical analysis of leaves of four populations of Vetiveria zizanioides （ introduced, local wild, domesticated wild populations of different years）. The re...Large sample sampling was adopted in this paper for the statistical analysis of leaves of four populations of Vetiveria zizanioides （ introduced, local wild, domesticated wild populations of different years）. The results showed that four populations varied in different degrees in terms of tiller height, leaf biomass, and leaf biomass ratio. Leaf length, leaf width, and ratio of leaf width to length of different populations also varied, but had similar power-function change regularity, and showed converging leaf biomass growth pattern and leaf growth process.展开更多
An experiment was conducted on Fluvisols of Awassa for two consecutive years （2005-2006） to determine effects of planting pattern and plant density on dry matter accumulation, nodulation, protein and oil content in ...An experiment was conducted on Fluvisols of Awassa for two consecutive years （2005-2006） to determine effects of planting pattern and plant density on dry matter accumulation, nodulation, protein and oil content in early and late maturing soybean varieties. Results indicated that Awassa-95 variety produced significantly higher （P 〈 0.05） number of nodules/plant （NDN）, nodule dry matter （NDM） and leaf dry matter （LDM at R2 （mid flowering） stage of soybean growth than that of variety Belessa-95）. Similarly, variety Awassa-95 （45%） produced significantly higher protein content than variety Belessa-95 （40%）. However, variety Belessa-95 accumulated significantly higher （P 〈 0.01） dry matter in straw, grain and total biomass at R7 （physiological maturity） stage of soybean growth than variety Awassa-95. Similarly, oil content of variety Belessa-95 （18.1%） was significantly （P 〈 0.05） higher than that of variety Awassa-95 （15.9%）. Equidistant rows produced significantly higher （P 〈 0.05） NDM than either rectangular or paired rows. Moreover, soybean plants grown in both rectangular and equidistant rows produced significantly higher （P 〈 0.01） straw dry matter than those grown in paired rows; but, grain dry matter/plant （GDM） was significantly higher （P 〈 0.01） paired and rectangular rows compared to equidistant rows. Plant density also affected the per plant GDM production as it was significantly higher （P 〈 0.01） in 20 and 30 plants/m2 than higher plant densities （40 and 50 plants/m2）. However, dry matter and yield components had strong negative association with protein content. In fact, strong positive correlation （R 〉 0.600） occurred between grain yield and its components with dry matter components at R2 （stem dry matter （SDM）, leaf dry matter （LDM） and stem ＋ nodule ＋ leaf dry matter together known as TDM） and straw dry matter at R7 in both varieties. This study depicted that soybean plants that produce higher dry matter components at R2 would probably produce more straw dry matter, greater grain yield components and higher grain yield dry matter at later stages.展开更多
文摘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.
文摘Leaf area index (LAI) of natural vegetation is recognized as the most important variable for measuring vegetation structure over large areas, and for relating it to energy and mass exchange, which has been successfully estimated from satellite resolution sensors. In this paper, according to the statistical analysis based on a lot of forest plots, the mathematical models of LAI distribution patterns in the hydro thermal spaces for five coniferous forest types in China were established. For the cold temperate larch forests growing in the dry and cold climate, their LAI increases with the increasing of warm index and precipitation in the way of hyperbolic quadratic surface. For the cold temperate spruce fir forests and temperate Pinus tabulaeformis forests, their LAI is negatively related to the annual mean air temperature in the way of the natural exponential curve, in order to adapt to the water oppressed environments. For the subtropical Pinus massoniana forests and Cunninghamia lanceolata forests growing in the warm and moist climate, their LAI is related to the annual mean air temperature in the way of the parabolic quadratic curve.
基金funded by China National Science and Technology Support Program(Grant No.2012BAD21B02)
文摘This study aimed to demonstrate change in spatial correlation between Korean pine （Pinus koraiensis Sieb. et Zucc.） and three rare species, and change in spatial distribution of four species in response to a range of selective cutting intensities. We sampled three plots of mixed Korean pine and broad-leaf forest in Lushuihe Forestry Bureau of Jilin province, China. Plot 1, a control, was unlogged Korean pine broad-leaf forest. In plots 2 and 3, Korean pine was selectively cut at 15 and 30 % intensity, respectively, in the 1970s. Other species were rarely cut. We used point-pattern analysis to research the spatial distributions of four tree species and quantify spatial correlations between Korean pine and the other three species, Amur linden （Tilia amurensis Rupr.）, Manchurian ash （Fraxinus mandshurica Rupr.）, and Mongolian oak （Quercus mongolica Fisch.） in all three plots. The results of the study show that selective cutting at 15 % intensity did not significantly change either the species spatial patterns or the spatial correlation between Korean pine and broadleaf species. Selective cutting at 30 % intensity slightly affected the growth of Korean pine and valuable species in forest communities, and the effect was considered nondestructive and recoverable.
文摘叶面积指数(Leaf Area Index, LAI)作为植被结构和生长状况的重要指标和生态参数,能够较好地反映植被的生长状况与分布情况,本文基于LAI反映城市绿色空间的分布状况,以乌鲁木齐市作为研究区,使用2016~2022年的Sentinel-2系列遥感数据反演了乌鲁木齐市夏季LAI时间序列数据。引入景观指数分析乌鲁木齐市2016~2022年不同等级绿色空间的组分与结构变化特征;使用结合Sen斜率估计的M-K趋势检验分析了乌鲁木齐市夏季LAI的变化趋势;并通过地理探测器分析了不同驱动因子对LAI的驱动作用。结果表明:(1)建成区内部多为低密度植被区域覆盖,高密度植被覆盖区域集中于城市边缘;(2)乌鲁木齐市的绿色空间面积主要呈减少趋势。(3)影响乌鲁木齐市绿色空间的驱动因子主要为土地利用类型和降水量;(4)交互探测表明驱动因子交互作用影响力强于单个驱动因子。
基金Foundation project： This paper was supported by National Natural Science Foundation of China （No. 30371126）.
文摘In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to administrate a hierarchical list of leaf images, some sorts of edge detection can be performed to identify the individual tokens of every image and the frame of the leaf can be got to differentiate the tree species. An approach based on back-propagation neuronal network is proposed and the programming language for the implementation is also Riven by using Java. The numerical simulations results have shown that the proposed leaf strategt is effective and feasible.
基金Sponsored by National Spark Program(2012GA780024)Program of Marginal Tropical Characteristic Plant Resources Development Center of Guangdong Universities and Colleges(GCZX-B1002)
文摘Large sample sampling was adopted in this paper for the statistical analysis of leaves of four populations of Vetiveria zizanioides （ introduced, local wild, domesticated wild populations of different years）. The results showed that four populations varied in different degrees in terms of tiller height, leaf biomass, and leaf biomass ratio. Leaf length, leaf width, and ratio of leaf width to length of different populations also varied, but had similar power-function change regularity, and showed converging leaf biomass growth pattern and leaf growth process.
文摘An experiment was conducted on Fluvisols of Awassa for two consecutive years （2005-2006） to determine effects of planting pattern and plant density on dry matter accumulation, nodulation, protein and oil content in early and late maturing soybean varieties. Results indicated that Awassa-95 variety produced significantly higher （P 〈 0.05） number of nodules/plant （NDN）, nodule dry matter （NDM） and leaf dry matter （LDM at R2 （mid flowering） stage of soybean growth than that of variety Belessa-95）. Similarly, variety Awassa-95 （45%） produced significantly higher protein content than variety Belessa-95 （40%）. However, variety Belessa-95 accumulated significantly higher （P 〈 0.01） dry matter in straw, grain and total biomass at R7 （physiological maturity） stage of soybean growth than variety Awassa-95. Similarly, oil content of variety Belessa-95 （18.1%） was significantly （P 〈 0.05） higher than that of variety Awassa-95 （15.9%）. Equidistant rows produced significantly higher （P 〈 0.05） NDM than either rectangular or paired rows. Moreover, soybean plants grown in both rectangular and equidistant rows produced significantly higher （P 〈 0.01） straw dry matter than those grown in paired rows; but, grain dry matter/plant （GDM） was significantly higher （P 〈 0.01） paired and rectangular rows compared to equidistant rows. Plant density also affected the per plant GDM production as it was significantly higher （P 〈 0.01） in 20 and 30 plants/m2 than higher plant densities （40 and 50 plants/m2）. However, dry matter and yield components had strong negative association with protein content. In fact, strong positive correlation （R 〉 0.600） occurred between grain yield and its components with dry matter components at R2 （stem dry matter （SDM）, leaf dry matter （LDM） and stem ＋ nodule ＋ leaf dry matter together known as TDM） and straw dry matter at R7 in both varieties. This study depicted that soybean plants that produce higher dry matter components at R2 would probably produce more straw dry matter, greater grain yield components and higher grain yield dry matter at later stages.
