Seagrass habitats are structurally complex ecosystems, which support high productivity and biodiversity. In temperate systems the density of seagrass may change seasonally, and this may influence the associated fish a...Seagrass habitats are structurally complex ecosystems, which support high productivity and biodiversity. In temperate systems the density of seagrass may change seasonally, and this may influence the associated fish and invertebrate community. Little is known about the role of seagrass beds as possible nursery areas for fish and invertebrates in China. To study the functioning of a seagrass habitat in northern China, demersal fish and invertebrates were collected monthly using traps, from February 2009 to January 2010. The density, leaf length and biomass of the dominant seagrass Zostera marina and water temperature were also measured. The study was conducted in a Seagrass Natural Reserve(SNR) on the eastern coast of the Shandong Peninsula, China. A total of 22 fish species and five invertebrate species were recorded over the year. The dominant fish species were Synechogobius ommaturus, Sebastes schlegelii, Pholis fangi, Pagrus major and Hexagrammos otakii and these species accounted for 87% of the total number of fish. The dominant invertebrate species were Charybdis japonica and Octopus variabilis and these accounted for 98% of the total abundance of invertebrates. There was high temporal variation in species composition and abundance. The peak number of fish species occurred in August–October 2009, while the number of individual fish and biomass was highest during November 2009. Invertebrate numbers and biomass was highest in March, April, July and September 2009. Temporal changes in species abundance of fishes and invertebrates corresponded with changes in the shoot density and leaf length of the seagrass, Zostera marina.展开更多
Kentucky bluegrass (Poa pratensis L.) is the most common perennial turfgrass species grown on playgrounds, municipal and residential lawn areas, and golf tees, fairways and roughs. Fertilization is the most efficient ...Kentucky bluegrass (Poa pratensis L.) is the most common perennial turfgrass species grown on playgrounds, municipal and residential lawn areas, and golf tees, fairways and roughs. Fertilization is the most efficient way to improve and maintain turfgrass aesthetic quality. Tissue diagnosis can guide fertilization, but tissue concentration ranges are biased by not taking into consideration nutrient inter-relationships, carryover effects and other key features. The centered log-ratio transformation reflects nutrient interactions in plants and avoids statistical biases. Machine learning (ML) models relate the target variable to the key features ex ante, and can predict future events from prior knowledge. The objective of his study was to predict turfgrass quality from key features and rank nutrients in the order of their limitations. The experimental setup comprised four N, three P, and four K rates applied on permanent plots during three consecutive years. Soils were a loam and an USGA sand. Eleven elements (N, S, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe) were quantified in clippings collected during spring, summer and autumn every year. Turfgrass quality was categorized as target variable by color rating. Concentrations were centered log-ratioed (clr) partitioned into four quadrants in the confusion matrix generated by the xgboost ML model. The area under curve (AUC) and model accuracy were high to predict turfgrass color from the nutrient analyses of clippings collected in the preceding season, facilitating the seasonal adjustment of the fertilization regime to sustain high turfgrass quality. We provide a computational example to run the ML model and rank nutrients in the order of their limitations.展开更多
The new shoot density of slash pine serves as a vital indicator for assessing its growth and photosynthetic capacity,while the number of new shoots offers an intuitive reflection of this density.With deep learning met...The new shoot density of slash pine serves as a vital indicator for assessing its growth and photosynthetic capacity,while the number of new shoots offers an intuitive reflection of this density.With deep learning methods becoming increasingly popular,automated counting of new shoots has greatly improved in recent years but is still limited by tedious and expensive data collection and labeling.To resolve these issues,this paper proposes a semi-supervised counting network(MTSC-Net)for estimating the number of slash pine new shoots.First,based on the mean-teacher framework,we introduce the improved VGG19 to extract multiscale new shoot features.Second,to connect local new shoot feature information with global channel features,attention feature fusion module is introduced to achieve effective feature fusion.Finally,the new shoot density map and density probability distribution are processed in a fine-grained manner through multiscale dilated convolution of the regression head and classification head.In addition,a masked image modeling strategy is introduced to encourage the contextual understanding of global new shoot features and improve the counting performance.The experimental results show that MTSC-Net outperforms other semi-supervised counting models with labeled percentages ranging from 5%to 50%.When the labeled percentage is 5%,the mean absolute error and root mean square error are 17.71 and 25.49,respectively.These findings demonstrate that our work can be used as an efficient semi-supervised counting method to provide automated support for tree breeding and genetic utilization.展开更多
基金Supported by the National Basic Research Program of China(973 Program)(No.2015CB453303)the National Special Research Fund for Non-Profit Marine Sector(No.201405010-2)the Science and Technology Development Program of Shandong(No.2014GSF117003)
文摘Seagrass habitats are structurally complex ecosystems, which support high productivity and biodiversity. In temperate systems the density of seagrass may change seasonally, and this may influence the associated fish and invertebrate community. Little is known about the role of seagrass beds as possible nursery areas for fish and invertebrates in China. To study the functioning of a seagrass habitat in northern China, demersal fish and invertebrates were collected monthly using traps, from February 2009 to January 2010. The density, leaf length and biomass of the dominant seagrass Zostera marina and water temperature were also measured. The study was conducted in a Seagrass Natural Reserve(SNR) on the eastern coast of the Shandong Peninsula, China. A total of 22 fish species and five invertebrate species were recorded over the year. The dominant fish species were Synechogobius ommaturus, Sebastes schlegelii, Pholis fangi, Pagrus major and Hexagrammos otakii and these species accounted for 87% of the total number of fish. The dominant invertebrate species were Charybdis japonica and Octopus variabilis and these accounted for 98% of the total abundance of invertebrates. There was high temporal variation in species composition and abundance. The peak number of fish species occurred in August–October 2009, while the number of individual fish and biomass was highest during November 2009. Invertebrate numbers and biomass was highest in March, April, July and September 2009. Temporal changes in species abundance of fishes and invertebrates corresponded with changes in the shoot density and leaf length of the seagrass, Zostera marina.
文摘Kentucky bluegrass (Poa pratensis L.) is the most common perennial turfgrass species grown on playgrounds, municipal and residential lawn areas, and golf tees, fairways and roughs. Fertilization is the most efficient way to improve and maintain turfgrass aesthetic quality. Tissue diagnosis can guide fertilization, but tissue concentration ranges are biased by not taking into consideration nutrient inter-relationships, carryover effects and other key features. The centered log-ratio transformation reflects nutrient interactions in plants and avoids statistical biases. Machine learning (ML) models relate the target variable to the key features ex ante, and can predict future events from prior knowledge. The objective of his study was to predict turfgrass quality from key features and rank nutrients in the order of their limitations. The experimental setup comprised four N, three P, and four K rates applied on permanent plots during three consecutive years. Soils were a loam and an USGA sand. Eleven elements (N, S, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe) were quantified in clippings collected during spring, summer and autumn every year. Turfgrass quality was categorized as target variable by color rating. Concentrations were centered log-ratioed (clr) partitioned into four quadrants in the confusion matrix generated by the xgboost ML model. The area under curve (AUC) and model accuracy were high to predict turfgrass color from the nutrient analyses of clippings collected in the preceding season, facilitating the seasonal adjustment of the fertilization regime to sustain high turfgrass quality. We provide a computational example to run the ML model and rank nutrients in the order of their limitations.
基金supported by the Shandong Province Natural Science Foundation Youth Branch under grant ZR20-23QF016the Biological Breeding-Major projects(Grant no.2023ZD040580105)the Fundamental Research Funds of CAF(CAFYBB2022QA001).
文摘The new shoot density of slash pine serves as a vital indicator for assessing its growth and photosynthetic capacity,while the number of new shoots offers an intuitive reflection of this density.With deep learning methods becoming increasingly popular,automated counting of new shoots has greatly improved in recent years but is still limited by tedious and expensive data collection and labeling.To resolve these issues,this paper proposes a semi-supervised counting network(MTSC-Net)for estimating the number of slash pine new shoots.First,based on the mean-teacher framework,we introduce the improved VGG19 to extract multiscale new shoot features.Second,to connect local new shoot feature information with global channel features,attention feature fusion module is introduced to achieve effective feature fusion.Finally,the new shoot density map and density probability distribution are processed in a fine-grained manner through multiscale dilated convolution of the regression head and classification head.In addition,a masked image modeling strategy is introduced to encourage the contextual understanding of global new shoot features and improve the counting performance.The experimental results show that MTSC-Net outperforms other semi-supervised counting models with labeled percentages ranging from 5%to 50%.When the labeled percentage is 5%,the mean absolute error and root mean square error are 17.71 and 25.49,respectively.These findings demonstrate that our work can be used as an efficient semi-supervised counting method to provide automated support for tree breeding and genetic utilization.
