Mango(Mangifera indica L.)is one of the main economic crops in Hainan,China,prized for its distinctive flavor and high nutritional value.It is also rich in health-promoting antioxidants such as vitamin C and flavonoid...Mango(Mangifera indica L.)is one of the main economic crops in Hainan,China,prized for its distinctive flavor and high nutritional value.It is also rich in health-promoting antioxidants such as vitamin C and flavonoids.Enhanced ultraviolet-B(UV—B)radiation,a growing global environmental concern,alters plant antioxidant systems,with increased flavonoid accumulation as a common adaptive response.However,its effects on mango fruit remain largely unexplored.To investigate the antioxidant responses of mango to enhanced UV-B radiation and identify key responsive flavonoid compounds and regulatory genes,we exposed‘Tainong 1’mango fruits growing under natural light to 96 kJ·m^(-2)·d^(-1)of UV-B radiation to simulate high UV-B conditions.Treated fruits were smaller in size and had a pulp of a more intense yellow colour.Further,malondialdehyde content in treated fruits was higher during the phase of rapid fruit enlargement.Additionally,treated fruits showed increased sugar-acid ratios,total phenol,total flavonoid,carotenoid,and ascorbic acid contents.Furthermore,they showed significantly enhanced antioxidant activity,as measured by the FRAP,ABTS,and DPPH assays.Extensive targeted metabolomic-analysis identified flavonoids as the largest category of compounds differentially expressed in treated and control groups.Quantitative metabolomics of flavonoids identified hyperoside,quercimeritrin,and(-)-catechin gallate as the key flavonoid metabolites responsive to UV-B treatment.Transcriptome analysis revealed an enrichment of the flavonoid biosynthesis pathway,with most associated differentially expressed genes showing upregulation.Furthermore,qRT-PCR analysis confirmed that the expression of the genes MiCHS7,MiCHI1,MiCHI2,MiFLS,MiF3H2,and MiF3H3 correlated with changes in key flavonoid metabolites.Indeed,correlation analysis indicated that MiCHS7,MiCHI1,MiFLS,and MiF3H3 are potential key genes involved in flavonoid accumulation under UV-B treatment.Thus,our study provides a theoretical basis for breeding for new resilient varieties and developing UV-B-resistant mango cultivation techniques.展开更多
Mango is a plant with high economic value in the agricultural industry;thus,it is necessary to maximize the productivity performance of the mango plant,which can be done by implementing artificial intelligence.In this...Mango is a plant with high economic value in the agricultural industry;thus,it is necessary to maximize the productivity performance of the mango plant,which can be done by implementing artificial intelligence.In this study,a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential,so that it becomes an early detection warning system that has an impact on increasing agricultural productivity.The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules,namely the C2S module.The C2S module consists of three sub-modules such as the convolutional block attention module(CBAM),the coordinate attention(CA)module,and the squeeze-and-excitation(SE)module.The dataset is constructed by eight classes,including seven classes of disease conditions and one class of health conditions.The experimental result shows that the proposed lightweight model has the optimal results,which increase by 13.15% of mAP50 compared to the original model YOLOv7-Tiny.While the mAP50:95 also achieved the highest results compared to other models,including YOLOv3-Tiny,YOLOv4-Tiny,YOLOv5,and YOLOv7-Tiny.The advantage of the proposed lightweightmodel is the adaptability that supports it in constrained environments,such as edge computing systems.This proposedmodel can support a robust,precise,and convenient precision agriculture system for the user.展开更多
Mango farming significantly contributes to the economy,particularly in developing countries.However,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at...Mango farming significantly contributes to the economy,particularly in developing countries.However,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial losses.The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture.This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)architectures.Two datasets were used.The first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery mildew.The second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and Rot.Both datasets were obtained from publicly available sources.The proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS dataset.The results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more efficiently.The system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual diagnostics.Additionally,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.展开更多
[Objectives]To preliminarily investigate the morphological identification and content determination of mango seeds utilized in Tibetan medicine,thereby providing foundational data to support the further refinement of ...[Objectives]To preliminarily investigate the morphological identification and content determination of mango seeds utilized in Tibetan medicine,thereby providing foundational data to support the further refinement of quality standards for mango seeds.[Methods]Powder microscopic examination,thin-layer chromatography(TLC),and high-performance liquid chromatography(HPLC)were employed to identify mango seeds sourced from various regions in Sichuan Province.In accordance with the 2020 edition of the Chinese Pharmacopoeia(Volume IV),the extract content,total ash,acid-insoluble ash,and moisture content of the mango seeds were quantitatively determined.[Results]The morphological and powder microscopic characteristics of mango seeds in Tibetan medicine were described in detail.The methanol extract was qualitatively identified using TLC,and the content of gallic acid in the medicinal samples was determined by HPLC.The total ash content of mango seeds ranged from 1.82%to 2.73%,while the acid-insoluble ash content varied between 0.08%and 0.55%.The extract content ranged from 12.16%to 24.06%,and the moisture content was between 6.75%and 8.98%.[Conclusions]Specifications for mango seeds in Tibetan medicine have been established,indicating that the total ash content should not exceed 4.0%,the acid-insoluble ash content should not exceed 2%,the content of dilute ethanol extract should be no less than 15.0%,the moisture content should not exceed 12.0%,and the gallic acid content should be at least 1%.These parameters serve as a foundation for the development of quality standards for mango seeds in Tibetan medicine.展开更多
In order to gain a deeper understanding of the impact of climatic conditions on mango cultivation in Jingdong County,according to the requirements for meteorological conditions from the biological characteristics of m...In order to gain a deeper understanding of the impact of climatic conditions on mango cultivation in Jingdong County,according to the requirements for meteorological conditions from the biological characteristics of mango trees,the climatic conditions of Jingdong station and the main mango production areas in Yunnan Province were compared,and the climatic characteristics in the high-and low-yield year were analyzed.The results show that in the middle and low altitude areas of Jingdong County,winter was dry and relatively warm,and summer was not extremely hot(the average temperature in the hottest month 23.8℃);the dry and wet seasons were distinct,and rainy and hot weather occurred in the same season(from June to September);there was sufficient sunshine in the winter half year.The main climatic advantages for mango cultivation in Jingdong County are manifested as follows:the overwintering temperature and light conditions were relatively favorable(the average temperature in the coldest month was 11.3℃,and average sunshine duration in the three months of winter was 6.3 h/d);the annual total heat was moderate,and≥10℃accumulated temperature was 6600℃·d;the temperature effectiveness during the main growing season of mangoes was relatively higher;the sunshine duration and climate humidity during the flower bud differentiation period were moderate(sunshine duration was 6.4 h/d,and monthly precipitation was 19.2 mm);the rainfall was abundant during the maturation period of fruits.The insufficient light and heat intensity during the maturation period of fruits(average sunshine duration was 4.4 h/d,and average temperature was 23.2℃from June to September),the susceptibility to the influence of spring drought during the young fruit stage of mangoes(precipitation was 141 mm from March to May),and relatively lower temperature during the flowering and pollination period in some years jointly constituted the main climatic constraints on local mango yield.展开更多
[Objective] The genetic diversity of major mango cultivars in China was analyzed by using SSR markers, and their fingerprints were constructed so as to provide theoretical basis for germplasm innovation and breeding o...[Objective] The genetic diversity of major mango cultivars in China was analyzed by using SSR markers, and their fingerprints were constructed so as to provide theoretical basis for germplasm innovation and breeding of mango. [Method] With 115 pairs of SSR primers, genetic diversity analysis and cluster analysis were performed for 30 mango cultivars, among which the genetic relationships were analyzed. [Result] Total 64 pairs of polymorphic primers were screened out from the 115 pairs of primers, and total 343 bands were amplified from the 30 cultivars with 73.2% of polymorphic bands. On average, 3.9 allelic loci were detected for each pair of primers with genetic diversity index of 0.5, Shannon＇s diversity index of 1.00 and polymorphism information content of 0.49, indicating higher genetic diversity. The cluster analysis showed that the 30 major cultivars could be classified into four categories. The first category included 14 cultivars; the second category included 11 cultivars, most of which were introduced from abroad; the third category included 4 cultivars, Le., Miansan, Parayinda, Baiyu and Hongxiangya： the fourth category included only one cultivar Maqiesu.By using 7 pairs of SSR markers, i.e., M42, M49, M54, M55, M96, M99 and M103, digital fingerprints were constructed for the 30 mango cultivars. [Conclusion] The 30 mango cultivars present more complex genomic genetics and abundant genetic information, and they have higher genetic diversity.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.32160677)the Hainan University Mango Research System.
文摘Mango(Mangifera indica L.)is one of the main economic crops in Hainan,China,prized for its distinctive flavor and high nutritional value.It is also rich in health-promoting antioxidants such as vitamin C and flavonoids.Enhanced ultraviolet-B(UV—B)radiation,a growing global environmental concern,alters plant antioxidant systems,with increased flavonoid accumulation as a common adaptive response.However,its effects on mango fruit remain largely unexplored.To investigate the antioxidant responses of mango to enhanced UV-B radiation and identify key responsive flavonoid compounds and regulatory genes,we exposed‘Tainong 1’mango fruits growing under natural light to 96 kJ·m^(-2)·d^(-1)of UV-B radiation to simulate high UV-B conditions.Treated fruits were smaller in size and had a pulp of a more intense yellow colour.Further,malondialdehyde content in treated fruits was higher during the phase of rapid fruit enlargement.Additionally,treated fruits showed increased sugar-acid ratios,total phenol,total flavonoid,carotenoid,and ascorbic acid contents.Furthermore,they showed significantly enhanced antioxidant activity,as measured by the FRAP,ABTS,and DPPH assays.Extensive targeted metabolomic-analysis identified flavonoids as the largest category of compounds differentially expressed in treated and control groups.Quantitative metabolomics of flavonoids identified hyperoside,quercimeritrin,and(-)-catechin gallate as the key flavonoid metabolites responsive to UV-B treatment.Transcriptome analysis revealed an enrichment of the flavonoid biosynthesis pathway,with most associated differentially expressed genes showing upregulation.Furthermore,qRT-PCR analysis confirmed that the expression of the genes MiCHS7,MiCHI1,MiCHI2,MiFLS,MiF3H2,and MiF3H3 correlated with changes in key flavonoid metabolites.Indeed,correlation analysis indicated that MiCHS7,MiCHI1,MiFLS,and MiF3H3 are potential key genes involved in flavonoid accumulation under UV-B treatment.Thus,our study provides a theoretical basis for breeding for new resilient varieties and developing UV-B-resistant mango cultivation techniques.
基金supported by National Science and Technology Council(NSTC)Taiwan,Grant No.NSTC 113-2221-E-167-023.
文摘Mango is a plant with high economic value in the agricultural industry;thus,it is necessary to maximize the productivity performance of the mango plant,which can be done by implementing artificial intelligence.In this study,a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential,so that it becomes an early detection warning system that has an impact on increasing agricultural productivity.The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules,namely the C2S module.The C2S module consists of three sub-modules such as the convolutional block attention module(CBAM),the coordinate attention(CA)module,and the squeeze-and-excitation(SE)module.The dataset is constructed by eight classes,including seven classes of disease conditions and one class of health conditions.The experimental result shows that the proposed lightweight model has the optimal results,which increase by 13.15% of mAP50 compared to the original model YOLOv7-Tiny.While the mAP50:95 also achieved the highest results compared to other models,including YOLOv3-Tiny,YOLOv4-Tiny,YOLOv5,and YOLOv7-Tiny.The advantage of the proposed lightweightmodel is the adaptability that supports it in constrained environments,such as edge computing systems.This proposedmodel can support a robust,precise,and convenient precision agriculture system for the user.
基金funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number(PNURSP2025R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Mango farming significantly contributes to the economy,particularly in developing countries.However,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial losses.The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture.This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)architectures.Two datasets were used.The first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery mildew.The second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and Rot.Both datasets were obtained from publicly available sources.The proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS dataset.The results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more efficiently.The system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual diagnostics.Additionally,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.
基金Supported by Key Research and Development Program Project of Sichuan Province(2024YFFK0190)Special Fund of Fundamental Research Funds for the Central Universities(ZYN2025257).
文摘[Objectives]To preliminarily investigate the morphological identification and content determination of mango seeds utilized in Tibetan medicine,thereby providing foundational data to support the further refinement of quality standards for mango seeds.[Methods]Powder microscopic examination,thin-layer chromatography(TLC),and high-performance liquid chromatography(HPLC)were employed to identify mango seeds sourced from various regions in Sichuan Province.In accordance with the 2020 edition of the Chinese Pharmacopoeia(Volume IV),the extract content,total ash,acid-insoluble ash,and moisture content of the mango seeds were quantitatively determined.[Results]The morphological and powder microscopic characteristics of mango seeds in Tibetan medicine were described in detail.The methanol extract was qualitatively identified using TLC,and the content of gallic acid in the medicinal samples was determined by HPLC.The total ash content of mango seeds ranged from 1.82%to 2.73%,while the acid-insoluble ash content varied between 0.08%and 0.55%.The extract content ranged from 12.16%to 24.06%,and the moisture content was between 6.75%and 8.98%.[Conclusions]Specifications for mango seeds in Tibetan medicine have been established,indicating that the total ash content should not exceed 4.0%,the acid-insoluble ash content should not exceed 2%,the content of dilute ethanol extract should be no less than 15.0%,the moisture content should not exceed 12.0%,and the gallic acid content should be at least 1%.These parameters serve as a foundation for the development of quality standards for mango seeds in Tibetan medicine.
基金Supported by the Meteorological Science and Technology Innovation Project of Pu'er Meteorological Bureau(PZ202416).
文摘In order to gain a deeper understanding of the impact of climatic conditions on mango cultivation in Jingdong County,according to the requirements for meteorological conditions from the biological characteristics of mango trees,the climatic conditions of Jingdong station and the main mango production areas in Yunnan Province were compared,and the climatic characteristics in the high-and low-yield year were analyzed.The results show that in the middle and low altitude areas of Jingdong County,winter was dry and relatively warm,and summer was not extremely hot(the average temperature in the hottest month 23.8℃);the dry and wet seasons were distinct,and rainy and hot weather occurred in the same season(from June to September);there was sufficient sunshine in the winter half year.The main climatic advantages for mango cultivation in Jingdong County are manifested as follows:the overwintering temperature and light conditions were relatively favorable(the average temperature in the coldest month was 11.3℃,and average sunshine duration in the three months of winter was 6.3 h/d);the annual total heat was moderate,and≥10℃accumulated temperature was 6600℃·d;the temperature effectiveness during the main growing season of mangoes was relatively higher;the sunshine duration and climate humidity during the flower bud differentiation period were moderate(sunshine duration was 6.4 h/d,and monthly precipitation was 19.2 mm);the rainfall was abundant during the maturation period of fruits.The insufficient light and heat intensity during the maturation period of fruits(average sunshine duration was 4.4 h/d,and average temperature was 23.2℃from June to September),the susceptibility to the influence of spring drought during the young fruit stage of mangoes(precipitation was 141 mm from March to May),and relatively lower temperature during the flowering and pollination period in some years jointly constituted the main climatic constraints on local mango yield.
基金Supported by Natural Science Foundation of Hainan Province(34128)Fundamental Scientific Research Funds of Chinese Academy of Tropical Agricultural Sciences(1630032013031)~~
文摘[Objective] The genetic diversity of major mango cultivars in China was analyzed by using SSR markers, and their fingerprints were constructed so as to provide theoretical basis for germplasm innovation and breeding of mango. [Method] With 115 pairs of SSR primers, genetic diversity analysis and cluster analysis were performed for 30 mango cultivars, among which the genetic relationships were analyzed. [Result] Total 64 pairs of polymorphic primers were screened out from the 115 pairs of primers, and total 343 bands were amplified from the 30 cultivars with 73.2% of polymorphic bands. On average, 3.9 allelic loci were detected for each pair of primers with genetic diversity index of 0.5, Shannon＇s diversity index of 1.00 and polymorphism information content of 0.49, indicating higher genetic diversity. The cluster analysis showed that the 30 major cultivars could be classified into four categories. The first category included 14 cultivars; the second category included 11 cultivars, most of which were introduced from abroad; the third category included 4 cultivars, Le., Miansan, Parayinda, Baiyu and Hongxiangya： the fourth category included only one cultivar Maqiesu.By using 7 pairs of SSR markers, i.e., M42, M49, M54, M55, M96, M99 and M103, digital fingerprints were constructed for the 30 mango cultivars. [Conclusion] The 30 mango cultivars present more complex genomic genetics and abundant genetic information, and they have higher genetic diversity.
