Z. Mamlić, V. Djukic, S. Vasiljevic, J. Miladinovic, M. Bajagic, G. Dozet, N. Djuric
Background: In order to reduce the use of synthetic and chemical agents in agriculture, more and more research is turning to ecological, more environmentally friendly methods. Plant aqueous extracts are products that can be a significant source of various elements, depending on the type and quality of soil on which the plant species from which the solution is prepared is grown. Methods: The aim of this study was to investigate the influence of aqueous extracts of different plant species on the yield and quality parameters of soybean seeds (Glycine max L.). Aqueous extracts of: nettle, nettle+comfrey, banana, banana peel, onion, willow and soybeans were used foliarly. The 1st foliar treatment plants was done when first flowers opened and the 2nd treatment was done when first pod reached final length. Result: The effect of aqueous extracts depends on the agroecological conditions and the analyzed traits. In 2020 the greatest effect was achieved on the free proline, SOD, Px and CAT. In 2021 the application of certain aqueous extracts had a significant effect on the yield, gerimination energy, germination percentage and vigour seed.
{"title":"Application of Plant Aqueous Extracts on Yield and Quality Parameters of Soybean Seeds (Glycine max L.)","authors":"Z. Mamlić, V. Djukic, S. Vasiljevic, J. Miladinovic, M. Bajagic, G. Dozet, N. Djuric","doi":"10.18805/lrf-767","DOIUrl":"https://doi.org/10.18805/lrf-767","url":null,"abstract":"Background: In order to reduce the use of synthetic and chemical agents in agriculture, more and more research is turning to ecological, more environmentally friendly methods. Plant aqueous extracts are products that can be a significant source of various elements, depending on the type and quality of soil on which the plant species from which the solution is prepared is grown. Methods: The aim of this study was to investigate the influence of aqueous extracts of different plant species on the yield and quality parameters of soybean seeds (Glycine max L.). Aqueous extracts of: nettle, nettle+comfrey, banana, banana peel, onion, willow and soybeans were used foliarly. The 1st foliar treatment plants was done when first flowers opened and the 2nd treatment was done when first pod reached final length. Result: The effect of aqueous extracts depends on the agroecological conditions and the analyzed traits. In 2020 the greatest effect was achieved on the free proline, SOD, Px and CAT. In 2021 the application of certain aqueous extracts had a significant effect on the yield, gerimination energy, germination percentage and vigour seed.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139789290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Mamlić, V. Djukic, S. Vasiljevic, J. Miladinovic, M. Bajagic, G. Dozet, N. Djuric
Background: In order to reduce the use of synthetic and chemical agents in agriculture, more and more research is turning to ecological, more environmentally friendly methods. Plant aqueous extracts are products that can be a significant source of various elements, depending on the type and quality of soil on which the plant species from which the solution is prepared is grown. Methods: The aim of this study was to investigate the influence of aqueous extracts of different plant species on the yield and quality parameters of soybean seeds (Glycine max L.). Aqueous extracts of: nettle, nettle+comfrey, banana, banana peel, onion, willow and soybeans were used foliarly. The 1st foliar treatment plants was done when first flowers opened and the 2nd treatment was done when first pod reached final length. Result: The effect of aqueous extracts depends on the agroecological conditions and the analyzed traits. In 2020 the greatest effect was achieved on the free proline, SOD, Px and CAT. In 2021 the application of certain aqueous extracts had a significant effect on the yield, gerimination energy, germination percentage and vigour seed.
{"title":"Application of Plant Aqueous Extracts on Yield and Quality Parameters of Soybean Seeds (Glycine max L.)","authors":"Z. Mamlić, V. Djukic, S. Vasiljevic, J. Miladinovic, M. Bajagic, G. Dozet, N. Djuric","doi":"10.18805/lrf-767","DOIUrl":"https://doi.org/10.18805/lrf-767","url":null,"abstract":"Background: In order to reduce the use of synthetic and chemical agents in agriculture, more and more research is turning to ecological, more environmentally friendly methods. Plant aqueous extracts are products that can be a significant source of various elements, depending on the type and quality of soil on which the plant species from which the solution is prepared is grown. Methods: The aim of this study was to investigate the influence of aqueous extracts of different plant species on the yield and quality parameters of soybean seeds (Glycine max L.). Aqueous extracts of: nettle, nettle+comfrey, banana, banana peel, onion, willow and soybeans were used foliarly. The 1st foliar treatment plants was done when first flowers opened and the 2nd treatment was done when first pod reached final length. Result: The effect of aqueous extracts depends on the agroecological conditions and the analyzed traits. In 2020 the greatest effect was achieved on the free proline, SOD, Px and CAT. In 2021 the application of certain aqueous extracts had a significant effect on the yield, gerimination energy, germination percentage and vigour seed.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"90 3-4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Variety characterization is the foremost important step that should be done by breeders to classify a variety into distinct groups. A significant technique for locating and assessing several genotypes for the registration, protection and production of seeds of superior quality is the Distinctness, Uniformity and Stability (DUS) characterization. Consequently, the current investigation aimed to use DUS descriptors to describe and assess the variance present in mungbean genotypes. Methods: One hundred forty-two mungbean genotypes were examined using 21 agro-morphological qualitative DUS descriptors in a randomized block design with two replications across two seasons, kharif 2021 and kharif 2022. Result: In the twenty-one DUS traits that were examined, four characters’ plant growth habit, leaf shape, leaf size and seed size exhibited trimorphic variance. Three characters (plant habit, stem pubescence and pod pubescence) were found to be identical among all genotypes while fourteen characters displayed dimorphic variance. All of the mungbean genotypes displayed a significant degree of variance for all DUS characteristics. Based on the UPGMA method of clustering, the dendrogram classified all the one hundred forty-two genotypes into three major clusters. The presence of variation among the genotypes under study was demonstrated by the further classification of these primary clusters into five sub-clusters. The majority of the genotypes were found in cluster II (121 genotypes), which was followed by cluster I (18 genotypes) and cluster III (3 genotypes).
背景：品种特征描述是育种者将一个品种划分为不同组别的最重要步骤。为注册、保护和生产优质种子而定位和评估多个基因型的一项重要技术是独特性、均匀性和稳定性（DUS）表征。因此，目前的调查旨在使用 DUS 描述因子来描述和评估绿豆基因型中存在的变异。方法：采用随机区组设计，在 2021 年和 2022 年两个季节进行两次重复，使用 21 个农业形态定性 DUS 描述因子对 142 个绿豆基因型进行了检测。结果在考察的 21 个 DUS 性状中，植物生长习性、叶形、叶片大小和种子大小四个性状表现出三态变异。在所有基因型中，有三个性状（植株习性、茎短柔毛和豆荚短柔毛）是相同的，而有 14 个性状表现出二态变异。所有绿豆基因型的所有 DUS 特征都显示出显著的变异程度。根据 UPGMA 聚类方法，树枝图将所有 142 个基因型分为三大类。将这些主聚类进一步划分为五个子聚类，证明了所研究的基因型之间存在差异。大多数基因型位于第 II 聚类（121 个基因型），其次是第 I 聚类（18 个基因型）和第 III 聚类（3 个基因型）。
{"title":"Morphological Characterization and Diversity Assessment of Mungbean [Vigna radiata (L.) Wilczek] Genotypes using DUS Descriptors as per PPV and FRA, 2001","authors":"Navreet Kaur Rai, Ravika, Rajesh Yadav, Amit, Karuna, Deepak Kaushik","doi":"10.18805/lr-5264","DOIUrl":"https://doi.org/10.18805/lr-5264","url":null,"abstract":"Background: Variety characterization is the foremost important step that should be done by breeders to classify a variety into distinct groups. A significant technique for locating and assessing several genotypes for the registration, protection and production of seeds of superior quality is the Distinctness, Uniformity and Stability (DUS) characterization. Consequently, the current investigation aimed to use DUS descriptors to describe and assess the variance present in mungbean genotypes. Methods: One hundred forty-two mungbean genotypes were examined using 21 agro-morphological qualitative DUS descriptors in a randomized block design with two replications across two seasons, kharif 2021 and kharif 2022. Result: In the twenty-one DUS traits that were examined, four characters’ plant growth habit, leaf shape, leaf size and seed size exhibited trimorphic variance. Three characters (plant habit, stem pubescence and pod pubescence) were found to be identical among all genotypes while fourteen characters displayed dimorphic variance. All of the mungbean genotypes displayed a significant degree of variance for all DUS characteristics. Based on the UPGMA method of clustering, the dendrogram classified all the one hundred forty-two genotypes into three major clusters. The presence of variation among the genotypes under study was demonstrated by the further classification of these primary clusters into five sub-clusters. The majority of the genotypes were found in cluster II (121 genotypes), which was followed by cluster I (18 genotypes) and cluster III (3 genotypes).\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"19 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Variety characterization is the foremost important step that should be done by breeders to classify a variety into distinct groups. A significant technique for locating and assessing several genotypes for the registration, protection and production of seeds of superior quality is the Distinctness, Uniformity and Stability (DUS) characterization. Consequently, the current investigation aimed to use DUS descriptors to describe and assess the variance present in mungbean genotypes. Methods: One hundred forty-two mungbean genotypes were examined using 21 agro-morphological qualitative DUS descriptors in a randomized block design with two replications across two seasons, kharif 2021 and kharif 2022. Result: In the twenty-one DUS traits that were examined, four characters’ plant growth habit, leaf shape, leaf size and seed size exhibited trimorphic variance. Three characters (plant habit, stem pubescence and pod pubescence) were found to be identical among all genotypes while fourteen characters displayed dimorphic variance. All of the mungbean genotypes displayed a significant degree of variance for all DUS characteristics. Based on the UPGMA method of clustering, the dendrogram classified all the one hundred forty-two genotypes into three major clusters. The presence of variation among the genotypes under study was demonstrated by the further classification of these primary clusters into five sub-clusters. The majority of the genotypes were found in cluster II (121 genotypes), which was followed by cluster I (18 genotypes) and cluster III (3 genotypes).
背景：品种特征描述是育种者将一个品种划分为不同组别的最重要步骤。为注册、保护和生产优质种子而定位和评估多个基因型的一项重要技术是独特性、均匀性和稳定性（DUS）表征。因此，目前的调查旨在使用 DUS 描述因子来描述和评估绿豆基因型中存在的变异。方法：采用随机区组设计，在 2021 年和 2022 年两个季节进行两次重复，使用 21 个农业形态定性 DUS 描述因子对 142 个绿豆基因型进行了检测。结果在考察的 21 个 DUS 性状中，植物生长习性、叶形、叶片大小和种子大小四个性状表现出三态变异。在所有基因型中，有三个性状（植株习性、茎短柔毛和豆荚短柔毛）是相同的，而有 14 个性状表现出二态变异。所有绿豆基因型的所有 DUS 特征都显示出显著的变异程度。根据 UPGMA 聚类方法，树枝图将所有 142 个基因型分为三大类。将这些主聚类进一步划分为五个子聚类，证明了所研究的基因型之间存在差异。大多数基因型位于第 II 聚类（121 个基因型），其次是第 I 聚类（18 个基因型）和第 III 聚类（3 个基因型）。
{"title":"Morphological Characterization and Diversity Assessment of Mungbean [Vigna radiata (L.) Wilczek] Genotypes using DUS Descriptors as per PPV and FRA, 2001","authors":"Navreet Kaur Rai, Ravika, Rajesh Yadav, Amit, Karuna, Deepak Kaushik","doi":"10.18805/lr-5264","DOIUrl":"https://doi.org/10.18805/lr-5264","url":null,"abstract":"Background: Variety characterization is the foremost important step that should be done by breeders to classify a variety into distinct groups. A significant technique for locating and assessing several genotypes for the registration, protection and production of seeds of superior quality is the Distinctness, Uniformity and Stability (DUS) characterization. Consequently, the current investigation aimed to use DUS descriptors to describe and assess the variance present in mungbean genotypes. Methods: One hundred forty-two mungbean genotypes were examined using 21 agro-morphological qualitative DUS descriptors in a randomized block design with two replications across two seasons, kharif 2021 and kharif 2022. Result: In the twenty-one DUS traits that were examined, four characters’ plant growth habit, leaf shape, leaf size and seed size exhibited trimorphic variance. Three characters (plant habit, stem pubescence and pod pubescence) were found to be identical among all genotypes while fourteen characters displayed dimorphic variance. All of the mungbean genotypes displayed a significant degree of variance for all DUS characteristics. Based on the UPGMA method of clustering, the dendrogram classified all the one hundred forty-two genotypes into three major clusters. The presence of variation among the genotypes under study was demonstrated by the further classification of these primary clusters into five sub-clusters. The majority of the genotypes were found in cluster II (121 genotypes), which was followed by cluster I (18 genotypes) and cluster III (3 genotypes).\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"29 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139795156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Machine learning has shown remarkable promise in recent years for use in areas such as pattern detection and categorization. The diagnosis of diseases is crucial in agriculture since they are a natural occurrence in plants. The easiest and most effective way to identify crop disease is through the use of image processing, computer vision and machine learning techniques. Methods: To identify and categorize cotton leaf diseases, the study compares the effectiveness of established techniques like Support Vector Machine (SVM) and random forest with state-of-the-art techniques like neural network (CNN) methods and architectures like Inceptionv3, VGG16 and RasNet50 with data augmentation and transfer learning. Result: The models were trained with four distinct types of plant photos that were manually gathered from a government agency and a farm. It was also noted that as the quantity of training data rose, so performed the resultant models.
{"title":"An Evaluation of Various Machine Learning Approaches for Detecting Leaf Diseases in Agriculture","authors":"Ok-Hue Cho","doi":"10.18805/lrf-787","DOIUrl":"https://doi.org/10.18805/lrf-787","url":null,"abstract":"Background: Machine learning has shown remarkable promise in recent years for use in areas such as pattern detection and categorization. The diagnosis of diseases is crucial in agriculture since they are a natural occurrence in plants. The easiest and most effective way to identify crop disease is through the use of image processing, computer vision and machine learning techniques. Methods: To identify and categorize cotton leaf diseases, the study compares the effectiveness of established techniques like Support Vector Machine (SVM) and random forest with state-of-the-art techniques like neural network (CNN) methods and architectures like Inceptionv3, VGG16 and RasNet50 with data augmentation and transfer learning. Result: The models were trained with four distinct types of plant photos that were manually gathered from a government agency and a farm. It was also noted that as the quantity of training data rose, so performed the resultant models.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"161 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139822729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Machine learning has shown remarkable promise in recent years for use in areas such as pattern detection and categorization. The diagnosis of diseases is crucial in agriculture since they are a natural occurrence in plants. The easiest and most effective way to identify crop disease is through the use of image processing, computer vision and machine learning techniques. Methods: To identify and categorize cotton leaf diseases, the study compares the effectiveness of established techniques like Support Vector Machine (SVM) and random forest with state-of-the-art techniques like neural network (CNN) methods and architectures like Inceptionv3, VGG16 and RasNet50 with data augmentation and transfer learning. Result: The models were trained with four distinct types of plant photos that were manually gathered from a government agency and a farm. It was also noted that as the quantity of training data rose, so performed the resultant models.
{"title":"An Evaluation of Various Machine Learning Approaches for Detecting Leaf Diseases in Agriculture","authors":"Ok-Hue Cho","doi":"10.18805/lrf-787","DOIUrl":"https://doi.org/10.18805/lrf-787","url":null,"abstract":"Background: Machine learning has shown remarkable promise in recent years for use in areas such as pattern detection and categorization. The diagnosis of diseases is crucial in agriculture since they are a natural occurrence in plants. The easiest and most effective way to identify crop disease is through the use of image processing, computer vision and machine learning techniques. Methods: To identify and categorize cotton leaf diseases, the study compares the effectiveness of established techniques like Support Vector Machine (SVM) and random forest with state-of-the-art techniques like neural network (CNN) methods and architectures like Inceptionv3, VGG16 and RasNet50 with data augmentation and transfer learning. Result: The models were trained with four distinct types of plant photos that were manually gathered from a government agency and a farm. It was also noted that as the quantity of training data rose, so performed the resultant models.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"17 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139882567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: The increasing demand for organic legume products has raised concerns about the validity of supply chains. This research explores the integration of blockchain and Artificial Intelligence (AI) technologies as a robust solution for ensuring the accuracy of organic legume products in supply chains. Leveraging the immutable and transparent nature of blockchain, the study establishes a decentralized ledger to record and validate each stage of the supply chain, from crop husbandry to distribution. Methods: Artificial intelligence (AI) algorithms are used in tandem to examine data points and identify irregularities that can signal the existence of fake goods. Through the integration of various technologies, the research aims to offer an advanced and flexible system that can anticipate and detect any risks to the validity of the product. Smart contract implementation on the blockchain enables automated verification procedures assuring, adherence to organic norms and laws. Result: Through case studies and empirical evidence, this paper demonstrates the efficacy of the proposed blockchain and AI integration in mitigating the risks associated with counterfeit organic legume products. This research contributes to the burgeoning field of blockchain and AI applications in supply chain management, offering a novel approach to fortify the integrity of organic food supply chains.
{"title":"Blockchain and Artificial Intelligence for Ensuring the Authenticity of Organic Legume Products in Supply Chains","authors":"Si-Yeong Kim, A. Alzubi","doi":"10.18805/lrf-786","DOIUrl":"https://doi.org/10.18805/lrf-786","url":null,"abstract":"Background: The increasing demand for organic legume products has raised concerns about the validity of supply chains. This research explores the integration of blockchain and Artificial Intelligence (AI) technologies as a robust solution for ensuring the accuracy of organic legume products in supply chains. Leveraging the immutable and transparent nature of blockchain, the study establishes a decentralized ledger to record and validate each stage of the supply chain, from crop husbandry to distribution. Methods: Artificial intelligence (AI) algorithms are used in tandem to examine data points and identify irregularities that can signal the existence of fake goods. Through the integration of various technologies, the research aims to offer an advanced and flexible system that can anticipate and detect any risks to the validity of the product. Smart contract implementation on the blockchain enables automated verification procedures assuring, adherence to organic norms and laws. Result: Through case studies and empirical evidence, this paper demonstrates the efficacy of the proposed blockchain and AI integration in mitigating the risks associated with counterfeit organic legume products. This research contributes to the burgeoning field of blockchain and AI applications in supply chain management, offering a novel approach to fortify the integrity of organic food supply chains.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"306 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140474130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: This study utilized advanced Artificial Intelligence (AI) techniques to develop predictive models for legume crop yields in the context of climate change scenarios. With the escalating challenges posed by climate change, accurately forecasting agricultural outcomes is imperative for sustainable food production. Methods: Utilizing an extensive dataset comprising legume crop yields, climate change forecasts and relevant environmental factors, this study employs advanced machine learning techniques such as XGBoost to create strong predictive models. The analysis encompasses diverse climate change scenarios to assess the resilience of legume crops under varying environmental conditions. Result: Results indicate a significant enhancement in predictive accuracy compared to conventional models, demonstrating the efficacy of AI in anticipating legume crop yields amidst climatic uncertainties. The presented work not only improves the precision of agricultural predictive modeling but also underscores the vital role of AI in mitigating the detrimental effects of climate change on food security. The agriculture industry faces changing weather patterns, thus using AI-powered prediction models becomes essential for making well-informed decisions and implementing sustainable farming methods.
{"title":"AI-Powered Predictive Modelling of Legume Crop Yields in a Changing Climate","authors":"Myung Hwan Na, In Seop Na","doi":"10.18805/lrf-790","DOIUrl":"https://doi.org/10.18805/lrf-790","url":null,"abstract":"Background: This study utilized advanced Artificial Intelligence (AI) techniques to develop predictive models for legume crop yields in the context of climate change scenarios. With the escalating challenges posed by climate change, accurately forecasting agricultural outcomes is imperative for sustainable food production. Methods: Utilizing an extensive dataset comprising legume crop yields, climate change forecasts and relevant environmental factors, this study employs advanced machine learning techniques such as XGBoost to create strong predictive models. The analysis encompasses diverse climate change scenarios to assess the resilience of legume crops under varying environmental conditions. Result: Results indicate a significant enhancement in predictive accuracy compared to conventional models, demonstrating the efficacy of AI in anticipating legume crop yields amidst climatic uncertainties. The presented work not only improves the precision of agricultural predictive modeling but also underscores the vital role of AI in mitigating the detrimental effects of climate change on food security. The agriculture industry faces changing weather patterns, thus using AI-powered prediction models becomes essential for making well-informed decisions and implementing sustainable farming methods.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"347 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: The study investigated codon usage bias in Medicago ruthenica transcriptome coding sequences, aiming to lay the foundation for optimizing codon composition and enhancing heterologous gene expression in Medicago ruthenica. Methods: In this research, Medicago ruthenica was used as the research material and 11,581 high-quality transcript gene sequences were selected from transcriptome data. Codon usage patterns and preferences were analyzed using software such as CodonW, R and Excel. Result: The study revealed that the effective number of codons (ENC) ranged from 28.8 to 61.0. The average GC content of codons in expressed genes of Medicago ruthenica was 0.40 and the average GC content of the third nucleotide position of synonymous codons (GC3s) was 0.33. Analysis through ENC-plot, neutrality plot and bias analysis suggested that codon usage bias in the Medicago ruthenica transcriptome may be the result of a combination of factors including selection and mutation. Fifteen optimal codons were selected, with ten ending in ‘A’ and five ending in ‘U’, indicating a preference for ‘A/U’ ending codons in the Medicago ruthenica transcriptome. The frequency of codon usage in Medicago ruthenica was compared to five other organisms, including Arabidopsis thaliana, Glycine max, Nicotiana tabacum, yeast and Escherichia coli, revealing significant differences with E. coli and relatively smaller differences with Nicotiana tabacum.
{"title":"Analysis of Codon Preferences in Medicago ruthenica based on Transcriptome Data","authors":"Xin Peng, Yingtong Mu, Feifei Wu, Nana Fu, Fengling Shi, Yutong Zhang","doi":"10.18805/lrf-776","DOIUrl":"https://doi.org/10.18805/lrf-776","url":null,"abstract":"Background: The study investigated codon usage bias in Medicago ruthenica transcriptome coding sequences, aiming to lay the foundation for optimizing codon composition and enhancing heterologous gene expression in Medicago ruthenica. Methods: In this research, Medicago ruthenica was used as the research material and 11,581 high-quality transcript gene sequences were selected from transcriptome data. Codon usage patterns and preferences were analyzed using software such as CodonW, R and Excel. Result: The study revealed that the effective number of codons (ENC) ranged from 28.8 to 61.0. The average GC content of codons in expressed genes of Medicago ruthenica was 0.40 and the average GC content of the third nucleotide position of synonymous codons (GC3s) was 0.33. Analysis through ENC-plot, neutrality plot and bias analysis suggested that codon usage bias in the Medicago ruthenica transcriptome may be the result of a combination of factors including selection and mutation. Fifteen optimal codons were selected, with ten ending in ‘A’ and five ending in ‘U’, indicating a preference for ‘A/U’ ending codons in the Medicago ruthenica transcriptome. The frequency of codon usage in Medicago ruthenica was compared to five other organisms, including Arabidopsis thaliana, Glycine max, Nicotiana tabacum, yeast and Escherichia coli, revealing significant differences with E. coli and relatively smaller differences with Nicotiana tabacum.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"323 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140471857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Moth bean (Vigna aconitifolia (Jaq.) Marechal) is characterized as one of the most drought hardy, short duration, annual legume crop. It is mainly grown in Northern districts of Karnataka. Moth bean crop suffers from many diseases viz., yellow mosaic, bacterial blight, root rot, anthracnose and powdery mildew. Moth bean is targeted by YMV which causes severe damage to grain and fodder yields. Since not much work has been carried out on characterization of moth bean yellow mosaic virus in Northern Karnataka, an attempt was made to partially characterize coat protein gene of Moth Bean Yellow Mosaic Virus (MBYMV). Methods: The total genomic DNA was extracted from leaf tissues of healthy moth bean plants and yellow mosaic virus infected plants utilizing by modified CTAB method. Specific primers for yellow mosaic viruses were tried to amplify coat protein region of MBYMV. Result: Moth bean leaf samples showing yellow mosaic symptoms gave positive results with MYMV specific primer pairs (MYMV-CP-F/MYMV-CP-R) and yielded amplicons of ~1000 bp. The 1000 bp PCR products were directly sequenced and assembled. Phylogenetic tree based on full length coat protein gene sequence of MBYMV with other geminiviruses sequences downloaded from NCBI Genbank formed three major clusters of MYMV, HgYMV and MYMIV. The present MBYMV isolate formed unique cluster with MYMV group.
{"title":"Molecular Detection and Partial Characterization of Coat Protein Gene of Moth Bean Yellow Mosaic Virus (MBYMV) from Northern Karnataka","authors":"H.K. Appu, G.U. Prema","doi":"10.18805/lr-5171","DOIUrl":"https://doi.org/10.18805/lr-5171","url":null,"abstract":"Background: Moth bean (Vigna aconitifolia (Jaq.) Marechal) is characterized as one of the most drought hardy, short duration, annual legume crop. It is mainly grown in Northern districts of Karnataka. Moth bean crop suffers from many diseases viz., yellow mosaic, bacterial blight, root rot, anthracnose and powdery mildew. Moth bean is targeted by YMV which causes severe damage to grain and fodder yields. Since not much work has been carried out on characterization of moth bean yellow mosaic virus in Northern Karnataka, an attempt was made to partially characterize coat protein gene of Moth Bean Yellow Mosaic Virus (MBYMV). Methods: The total genomic DNA was extracted from leaf tissues of healthy moth bean plants and yellow mosaic virus infected plants utilizing by modified CTAB method. Specific primers for yellow mosaic viruses were tried to amplify coat protein region of MBYMV. Result: Moth bean leaf samples showing yellow mosaic symptoms gave positive results with MYMV specific primer pairs (MYMV-CP-F/MYMV-CP-R) and yielded amplicons of ~1000 bp. The 1000 bp PCR products were directly sequenced and assembled. Phylogenetic tree based on full length coat protein gene sequence of MBYMV with other geminiviruses sequences downloaded from NCBI Genbank formed three major clusters of MYMV, HgYMV and MYMIV. The present MBYMV isolate formed unique cluster with MYMV group.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"63 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140471556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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