B. Nathawat, Dinesh Kumar, Rakesh Kumar, Ravinder Kumar, Raja Ram Choudhary
Background: Groundnut is an economically important edible oilseed crop. Groundnut suffers from seed, soil and foliar diseases. Among the groundnut diseases, collar rot is one of the economically important diseases. Collar rot is damaged regularly due to its seed and soil-borne nature. This disease is prevalent in almost all groundnut-growing states. Collar rot disease of groundnut is one of the most serious, destructive diseases and yield losses range from 13 to 52% and can be as high as 93.6% in some areas. Being mainly a soil-inhabiting pathogen, many environmental and soil factors are responsible for disease development. Methods: Field experiment was conducted for four years to find out effective control of collar rot of groundnut. Eight treatments including fungicides/bio agents along with control were laid in randomized block design with three replications. Efficacy of deep summer ploughing with mould board plough+Seed treatment with Tebuconazole followed by PGPR+Soil application of Trichoderma enriched in 250 kg FYM/ha at 35 and 80 DAS along with farmer practices as well as control was tested at ARS, Bikaner in RBD design during kharif season from 2017 to 2020 for management of collar rot diseases of groundnut. Result: The result of experiment revealed that deep summer ploughing with mould board plough+Soil application of Trichoderma @ 4 kg/ ha enriched in 250 kg FYM/ha+Seed treatment with Tebuconazole 2 DS @ 1.5 g/kg of seed followed by seed treatment with PGPR @ 625 g/ha of seed+Soil application of Trichoderma @ 4 kg/ha enriched in 250 kg FYM/ha at 35 and 80 DAS (T4) gave maximum germination (93.00%), minimum collar rot incidence (6.17%) and highest pod yield (4066.3 kg/ha) followed by deep summer ploughing with mould board plough+Seed treatment with Tebuconazole 2DS 1.5g/kg seed followed by PGPR @ 625 g/ha of seed+Soil application of Trichoderma @ 4 kg/ ha enriched in 250 kg FYM/ha at 35 and 80 DAS (T1) where germination (89.30%), collar rot (7.04%) and with pod yield (3661.9 kg/ha). All the treatment significantly were superior as compared to control, where minimum germination (82.00), maximum collar rot incidence (15.59 %) and minimum pod yield (2125.1 kg/ha) were recorded. As regard to ICBR, in different treatments, maximum ICBR ratio (1:20.68) was recorded in treatment consisting (T4): Deep summer ploughing with mould board plough+Seed treatment with Tebuconazole 1.5 g/kg seeds+Soil application of Trichoderma @ 4 kg/ha enriched in 250 kg FYM/ha at 35 and 80 DAS followed by treatment (T1).
{"title":"Management of Collar Rot Disease in Groundnut (Arachis hypogaea L.) Caused by Aspergillus niger in Rajasthan Through Bio-control Agents","authors":"B. Nathawat, Dinesh Kumar, Rakesh Kumar, Ravinder Kumar, Raja Ram Choudhary","doi":"10.18805/lr-5222","DOIUrl":"https://doi.org/10.18805/lr-5222","url":null,"abstract":"Background: Groundnut is an economically important edible oilseed crop. Groundnut suffers from seed, soil and foliar diseases. Among the groundnut diseases, collar rot is one of the economically important diseases. Collar rot is damaged regularly due to its seed and soil-borne nature. This disease is prevalent in almost all groundnut-growing states. Collar rot disease of groundnut is one of the most serious, destructive diseases and yield losses range from 13 to 52% and can be as high as 93.6% in some areas. Being mainly a soil-inhabiting pathogen, many environmental and soil factors are responsible for disease development. Methods: Field experiment was conducted for four years to find out effective control of collar rot of groundnut. Eight treatments including fungicides/bio agents along with control were laid in randomized block design with three replications. Efficacy of deep summer ploughing with mould board plough+Seed treatment with Tebuconazole followed by PGPR+Soil application of Trichoderma enriched in 250 kg FYM/ha at 35 and 80 DAS along with farmer practices as well as control was tested at ARS, Bikaner in RBD design during kharif season from 2017 to 2020 for management of collar rot diseases of groundnut. Result: The result of experiment revealed that deep summer ploughing with mould board plough+Soil application of Trichoderma @ 4 kg/ ha enriched in 250 kg FYM/ha+Seed treatment with Tebuconazole 2 DS @ 1.5 g/kg of seed followed by seed treatment with PGPR @ 625 g/ha of seed+Soil application of Trichoderma @ 4 kg/ha enriched in 250 kg FYM/ha at 35 and 80 DAS (T4) gave maximum germination (93.00%), minimum collar rot incidence (6.17%) and highest pod yield (4066.3 kg/ha) followed by deep summer ploughing with mould board plough+Seed treatment with Tebuconazole 2DS 1.5g/kg seed followed by PGPR @ 625 g/ha of seed+Soil application of Trichoderma @ 4 kg/ ha enriched in 250 kg FYM/ha at 35 and 80 DAS (T1) where germination (89.30%), collar rot (7.04%) and with pod yield (3661.9 kg/ha). All the treatment significantly were superior as compared to control, where minimum germination (82.00), maximum collar rot incidence (15.59 %) and minimum pod yield (2125.1 kg/ha) were recorded. As regard to ICBR, in different treatments, maximum ICBR ratio (1:20.68) was recorded in treatment consisting (T4): Deep summer ploughing with mould board plough+Seed treatment with Tebuconazole 1.5 g/kg seeds+Soil application of Trichoderma @ 4 kg/ha enriched in 250 kg FYM/ha at 35 and 80 DAS followed by treatment (T1).\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"47 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799648","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}
H. Shekhawat, Vijay Kamal Meena, K. Choudhary, Bhanu Prakash Sharma, Jitendra Kumar Sharma, Suresh Chand Meena
Background: Chickpea is grown in diverse agro-climatic conditions and various varieties respond differently to these environments. Stability analysis identifies adaptable varieties that yield consistently across regions. Our study focuses on identifying high-yield, reliable chickpea varieties through extensive multi-environment trials Methods: To accomplish our research goal, we conducted a comprehensive stability analysis during 2020-21 and 2021-22 using 12 different methods which include parametric, non-parametric and PCA-based AMMI, GGE biplot analysis. This extensive analysis was applied to six distinct Chickpea varieties cultivated in three diverse environments. Result: CSJ515 consistently demonstrated stability in all 10 analyses, while GNG2144 and RSG959 showed stability in 7 analyses. GNG1958 exhibited stability in four analyses and GNG2171 and RSG974 were stable in 3 and 2 analyses, respectively. These results suggest that CSJ515, GNG2144 and RSG959 have broad and stable adaptability across diverse environments.
{"title":"Assessment of Genetic Stability in Chickpea Varieties Through GGE and AMMI Analyses","authors":"H. Shekhawat, Vijay Kamal Meena, K. Choudhary, Bhanu Prakash Sharma, Jitendra Kumar Sharma, Suresh Chand Meena","doi":"10.18805/lr-5246","DOIUrl":"https://doi.org/10.18805/lr-5246","url":null,"abstract":"Background: Chickpea is grown in diverse agro-climatic conditions and various varieties respond differently to these environments. Stability analysis identifies adaptable varieties that yield consistently across regions. Our study focuses on identifying high-yield, reliable chickpea varieties through extensive multi-environment trials Methods: To accomplish our research goal, we conducted a comprehensive stability analysis during 2020-21 and 2021-22 using 12 different methods which include parametric, non-parametric and PCA-based AMMI, GGE biplot analysis. This extensive analysis was applied to six distinct Chickpea varieties cultivated in three diverse environments. Result: CSJ515 consistently demonstrated stability in all 10 analyses, while GNG2144 and RSG959 showed stability in 7 analyses. GNG1958 exhibited stability in four analyses and GNG2171 and RSG974 were stable in 3 and 2 analyses, respectively. These results suggest that CSJ515, GNG2144 and RSG959 have broad and stable adaptability across diverse environments.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809428","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}
P. Priyanka, S. Kokilavani, V. Geethalakshmi, M. K. Kalarani, G. Dheebakaran, N. K. Sathyamoorthy, B. Arthirani, S. D. Dharshini, K. Pugazenthi
Background: Abiotic stress negatively impacts the morphological, physiological and biochemical characteristics of plants leading to loss of yield and quality. The substantial increase in population and global surface temperature extends to global food insecurity hence it is important to maintain a sustainable yield of crops. Cowpea being a protein-rich legume and a nodule-forming crop facilitates not only meeting food insecurity but also creates a sustainable environment. The objective of this study was to explore the suitable management practice for cowpea to attain sustainable yield under the elevated temperature of +2°C from ambient. Methods: An experiment in cowpea variety Co 7 was carried out in Temperature Gradient Tunnel (TGT) located at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore. The research trial was carried out during the year 2021 and 2022 with Factorial Completely Randomized Design (FCRD). In this study, we have investigated the effect of different treatments on growth, yield, quality and available nutrients in soil. Result: The application of vermicompost with foliar spray of 3% Panchagavya at 30, 45 and 60 DAS stimulated the plant height, number of leaves and leaf area index (LAI). The increase in availability of soil nutrients exhibited higher dry matter production, pod length (10.46 cm), number of seeds per pod (7.56), test weight (12.56 g), seed yield (13.25 g plant-1) and seed protein (21.82%). These results suggest that vermicompost application with 3% foliar spray of Panchagavya has a positive effect on improving the high-temperature tolerance of cowpea plants.
{"title":"Exploring the Effective Management Strategy to Sustain Cowpea Production under High Temperature Stress","authors":"P. Priyanka, S. Kokilavani, V. Geethalakshmi, M. K. Kalarani, G. Dheebakaran, N. K. Sathyamoorthy, B. Arthirani, S. D. Dharshini, K. Pugazenthi","doi":"10.18805/lr-5231","DOIUrl":"https://doi.org/10.18805/lr-5231","url":null,"abstract":"Background: Abiotic stress negatively impacts the morphological, physiological and biochemical characteristics of plants leading to loss of yield and quality. The substantial increase in population and global surface temperature extends to global food insecurity hence it is important to maintain a sustainable yield of crops. Cowpea being a protein-rich legume and a nodule-forming crop facilitates not only meeting food insecurity but also creates a sustainable environment. The objective of this study was to explore the suitable management practice for cowpea to attain sustainable yield under the elevated temperature of +2°C from ambient. Methods: An experiment in cowpea variety Co 7 was carried out in Temperature Gradient Tunnel (TGT) located at Agro Climate Research Centre, Tamil\u0000Nadu Agricultural University, Coimbatore. The research trial was carried out during the year 2021 and 2022 with Factorial Completely Randomized Design (FCRD). In this study, we have investigated the effect of different treatments on growth, yield, quality and available nutrients in soil. Result: The application of vermicompost with foliar spray of 3% Panchagavya at 30, 45 and 60 DAS stimulated the plant height, number of leaves and leaf area index (LAI). The increase in availability of soil nutrients exhibited higher dry matter production, pod length (10.46 cm), number of seeds per pod (7.56), test weight (12.56 g), seed yield (13.25 g plant-1) and seed protein (21.82%). These results suggest that vermicompost application with 3% foliar spray of Panchagavya has a positive effect on improving the high-temperature tolerance of cowpea plants.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"30 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808411","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}
K. Malarkodi, V. Divya, K. Natarajan, K. N. Navamaniraj, K. Ganesan, M. Bhaskaran, R. Vigneshwari, D. T. Selvi, S. Kavitha
Background: Groundnut, also known as peanut is a vital crop worldwide, valued for its oil and protein-rich seeds. However, globally the production of groundnut is constrained by a number of biotic and abiotic factors, which significantly reduce yield. Among these, seed borne pathogen plays a major role.In order to manage plant disease and increase the yield, chitosan was used in this study since it is a natural polymer derived from chitin found in crustacean shells. Methods: Groundnut seeds were treated with chitosan 1.5 and 2.0% along with bio-control agents and carbendazim.Treated seeds were sown in field and observations viz., disease incidence (%), field emergence (%), plant height, flowering characters, yield attributing pod and seed characters were recorded. Result: The results revealed that no disease incidence was recorded in chitosan treated seeds. Seeds treated with chitosan 2% increased the field emergence and plant height up to 13 and 17 per cent over control, respectively. Chitosan 2% treated seeds-initiated flowers 4 days earlier than the control seeds and also quickly attained the 50% flowering. Apart from this, seeds treated with chitosan 2% increased the pod yield and seed yield ha-1 up to 27 and 29 per cent, respectively. In between the biocontrol agents, Bacillus subtilis showed an increased yield and yield attributing parameters.
{"title":"Assessing the Effects of Chitosan on Groundnut (Arachis hypogaea L.) Growth and Productivity","authors":"K. Malarkodi, V. Divya, K. Natarajan, K. N. Navamaniraj, K. Ganesan, M. Bhaskaran, R. Vigneshwari, D. T. Selvi, S. Kavitha","doi":"10.18805/lr-5266","DOIUrl":"https://doi.org/10.18805/lr-5266","url":null,"abstract":"Background: Groundnut, also known as peanut is a vital crop worldwide, valued for its oil and protein-rich seeds. However, globally the production of groundnut is constrained by a number of biotic and abiotic factors, which significantly reduce yield. Among these, seed borne pathogen plays a major role.In order to manage plant disease and increase the yield, chitosan was used in this study since it is a natural polymer derived from chitin found in crustacean shells. Methods: Groundnut seeds were treated with chitosan 1.5 and 2.0% along with bio-control agents and carbendazim.Treated seeds were sown in field and observations viz., disease incidence (%), field emergence (%), plant height, flowering characters, yield attributing pod and seed characters were recorded. Result: The results revealed that no disease incidence was recorded in chitosan treated seeds. Seeds treated with chitosan 2% increased the field emergence and plant height up to 13 and 17 per cent over control, respectively. Chitosan 2% treated seeds-initiated flowers 4 days earlier than the control seeds and also quickly attained the 50% flowering. Apart from this, seeds treated with chitosan 2% increased the pod yield and seed yield ha-1 up to 27 and 29 per cent, respectively. In between the biocontrol agents, Bacillus subtilis showed an increased yield and yield attributing parameters.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"38 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810064","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}
S. Jakhar, Sarvesh Tripathy, Barkha Sharma, C. R. Kantwa, R. Ghaswa, R. S. Bhadauria, Sushil Kumar, Gyanendra Pratap Tiwari
Background: Chickpea (Cicer arietinum L.) is a premier legume crop of Fabaceae family. It is also known as gram and Kabuli chana. It is one of the major rabi season pulse crop of the Ratlam district. The main objective of this study is the enhance production and productivity of chickpea through CFLDs with latest and specific technologies viz- large seeded high yielding varieties and resistance to fusarium wilt (RVG 202) chickpea variety under best package of practices. The major problem of ratlam district is productivity of chickpea is very low because of non-adoption of latest intervention by the farmer’s like use wilt resistant variety, imbalance use of plant nutrient, water stress at critical stage, infestation of weeds and incidence of pest. Methods: The present study was carried out by the Krishi Vigyan Kendra, Jaora, Ratlam District (M.P) during rabi season. Total 110 demonstration in farmer’s field of ratlam district during two years i.e., from rabi, 2020-21, rabi 2021-22 on integrated crop management (ICM). Front line demonstration on chickpea were organized five cluster of ratlam district. The demonstration intervention technology is improved variety (RVG-202), Optimum seed rate 80 kg/ha, properly seed treatment, crop nutrient management RDF as per STV, water management at critical stages, weed management and application of IDM module for the management of disease. Result: The results of study show a positive impact onfarming community due to the significant enhancement in crop yield greater than farmer. Results of the study revealed that the interventions increase demonstration field seed yield of chickpea by 30.76 and 32.71% over the farmers' field respectively, both the years. The average intervention technology gap (3.04 q/ha) extension gap 4.08 q/ha) and Technology index 15.22% suggested further improvement in the extension activities. The annual average benefit: cost ratio was higher (2.43 and 2.52) of the demonstrated plot compare to farmers plot (1.95 and 1.97), respectively. The similar trend was observed in terms of gross and net income returns which was demonstration plot is Rs 94589 Rs 67385 and under farmer's plot it was Rs 72783 and Rs. 49283 respectively.
{"title":"Cluster Frontline Demonstration: An Effective Technology Dissemination Approach for Maximization of Productivity and Profitability of Chickpea (Cicer arietinum L.)","authors":"S. Jakhar, Sarvesh Tripathy, Barkha Sharma, C. R. Kantwa, R. Ghaswa, R. S. Bhadauria, Sushil Kumar, Gyanendra Pratap Tiwari","doi":"10.18805/lr-5304","DOIUrl":"https://doi.org/10.18805/lr-5304","url":null,"abstract":"Background: Chickpea (Cicer arietinum L.) is a premier legume crop of Fabaceae family. It is also known as gram and Kabuli chana. It is one of the major rabi season pulse crop of the Ratlam district. The main objective of this study is the enhance production and productivity of chickpea through CFLDs with latest and specific technologies viz- large seeded high yielding varieties and resistance to fusarium wilt (RVG 202) chickpea variety under best package of practices. The major problem of ratlam district is productivity of chickpea is very low because of non-adoption of latest intervention by the farmer’s like use wilt resistant variety, imbalance use of plant nutrient, water stress at critical stage, infestation of weeds and incidence of pest. Methods: The present study was carried out by the Krishi Vigyan Kendra, Jaora, Ratlam District (M.P) during rabi season. Total 110 demonstration in farmer’s field of ratlam district during two years i.e., from rabi, 2020-21, rabi 2021-22 on integrated crop management (ICM). Front line demonstration on chickpea were organized five cluster of ratlam district. The demonstration intervention technology is improved variety (RVG-202), Optimum seed rate 80 kg/ha, properly seed treatment, crop nutrient management RDF as per STV, water management at critical stages, weed management and application of IDM module for the management of disease. Result: The results of study show a positive impact onfarming community due to the significant enhancement in crop yield greater than farmer. Results of the study revealed that the interventions increase demonstration field seed yield of chickpea by 30.76 and 32.71% over the farmers' field respectively, both the years. The average intervention technology gap (3.04 q/ha) extension gap 4.08 q/ha) and Technology index 15.22% suggested further improvement in the extension activities. The annual average benefit: cost ratio was higher (2.43 and 2.52) of the demonstrated plot compare to farmers plot (1.95 and 1.97), respectively. The similar trend was observed in terms of gross and net income returns which was demonstration plot is Rs 94589 Rs 67385 and under farmer's plot it was Rs 72783 and Rs. 49283 respectively.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"41 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809743","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}
The sustainable agriculture practices demands new innovations identifying plant diseases and instead of crop disease detection and precision and efficacy. An extensive review of the literature found through PubMed searches indicates a gap in the present approaches, which highlights the need for sophisticated machine learning solutions in the field of plant pathology. This study involves a comprehensive review of relevant publications collected via PubMed searches. The methodology involves the analysis of machine learning algorithms, datasets utilized and techniques applied for plant disease detection. Special attention is given to recent advancements in the field, focusing on the development and optimization of models tailored for precise and reliable disease identification. The study reveals compelling results, underscoring the transformative impact of machine learning innovations on plant disease detection accuracy. Specific algorithms exhibit superior performance, with implications for widespread applications in precision agriculture. These outcomes not only enhance current disease identification capabilities but also lay the groundwork for future advancements in automated and high-precision plant pathology diagnostics. The integration of machine learning emerges as a pivotal force in reshaping the landscape of plant disease detection.
{"title":"Machine Learning Innovations for Precise Plant Disease Detection: A Review","authors":"Wan-Bum Lee, Bong-Hyun Kim","doi":"10.18805/lrf-799","DOIUrl":"https://doi.org/10.18805/lrf-799","url":null,"abstract":"The sustainable agriculture practices demands new innovations identifying plant diseases and instead of crop disease detection and precision and efficacy. An extensive review of the literature found through PubMed searches indicates a gap in the present approaches, which highlights the need for sophisticated machine learning solutions in the field of plant pathology. This study involves a comprehensive review of relevant publications collected via PubMed searches. The methodology involves the analysis of machine learning algorithms, datasets utilized and techniques applied for plant disease detection. Special attention is given to recent advancements in the field, focusing on the development and optimization of models tailored for precise and reliable disease identification. The study reveals compelling results, underscoring the transformative impact of machine learning innovations on plant disease detection accuracy. Specific algorithms exhibit superior performance, with implications for widespread applications in precision agriculture. These outcomes not only enhance current disease identification capabilities but also lay the groundwork for future advancements in automated and high-precision plant pathology diagnostics. The integration of machine learning emerges as a pivotal force in reshaping the landscape of plant disease detection.","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"17 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816512","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 fusarium wilt disease of chickpea leaves is a common illness that leads to economic problems for farmers due decreased crop yield. Early disease detection and the implementation of suitable precautions can help to increase the yield of chickpeas. This study offers an improved method for Fusarium wilt disease prediction based on severity level using a convolutional neural learning algorithm. Methods: The Convolutional Neural Network (CNN) model is utilized in this work to identify leaf disease due to wilting. The dataset contains 4,339 images of chickpea leaves that were obtained from Kaggle. After preprocessing, the data is sent into the network model for training. The model shows acceptable classification and accuracy metrics. Result: Deep learning methods are very useful tools for tracking leaf diseases at their early stages and can help farmers with the use of controlling methods. The proposed work looks for changes in the shape and color of chickpea leaves in order to predict severe fusarium disease. Training and validation accuracies show a balanced trade-off by giving satisfactory outcomes. The model shows an overall accuracy of 74.79%. The confusion matrix and classification parameters increase the model’s performance.
{"title":"Classification and Severity Level Assessment of Fusarium Wilt Disease in Chickpeas using Convolutional Neural Network","authors":"A. Alzubi, Radha Raghuramapatruni, Pushpa Kumari","doi":"10.18805/lrf-807","DOIUrl":"https://doi.org/10.18805/lrf-807","url":null,"abstract":"Background: The fusarium wilt disease of chickpea leaves is a common illness that leads to economic problems for farmers due decreased crop yield. Early disease detection and the implementation of suitable precautions can help to increase the yield of chickpeas. This study offers an improved method for Fusarium wilt disease prediction based on severity level using a convolutional neural learning algorithm. Methods: The Convolutional Neural Network (CNN) model is utilized in this work to identify leaf disease due to wilting. The dataset contains 4,339 images of chickpea leaves that were obtained from Kaggle. After preprocessing, the data is sent into the network model for training. The model shows acceptable classification and accuracy metrics. Result: Deep learning methods are very useful tools for tracking leaf diseases at their early stages and can help farmers with the use of controlling methods. The proposed work looks for changes in the shape and color of chickpea leaves in order to predict severe fusarium disease. Training and validation accuracies show a balanced trade-off by giving satisfactory outcomes. The model shows an overall accuracy of 74.79%. The confusion matrix and classification parameters increase the model’s performance.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"16 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816927","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}
Rakesh Kumar Yadav, M. K. Tripathi, S. Tiwari, Ruchi Asati, Niraj Tripathi, R. S. Sikarwar
Background: Cicer arietinum (L.), is a legume being grown world wide as a good source of vegan protein. It is a vital part of human feed. Quantification of biochemical parameters of seed is one of the requirements for breeding programmes to develop cultivars suitable for human consumption and food industry. The objective of the present investigation was to evaluate chickpea genotypes on the basis of biochemical parameters to select superior germplasm for further crop improvement. Methods: In this investigation, seventy-one chickpea genotypes employed for different biochemical parameters analysis including protein, total free amino acid and sugar content, reducing and non-reducing sugar, phytic acid, total phenol, flavonoid and tannin content along with DPPH radical scavenging activities. Result: Genotypes showed substantial variation for different biochemical parameters. Maximum seed protein content was found in genotype JG315 (25.1%) and lowest in SAGL-152344 (14.3%), whilst content of amino acid ranged between 2.4 mg/g (SAGL-152318) to 9.51 mg/g (SAGL-152330). Maximum phytic acid content was evident in genotype SAGL22-122 (20.7 mg/g) and lowest in JG315 (4.78 mg/g). Range of total phenol content varied between 0.72mg/g (RVSSG 92) to 1.91 mg/g (ICCV20116).
{"title":"Estimation of Biochemical Parameters in Chickpea (Cicer arietinum L.) Genotypes","authors":"Rakesh Kumar Yadav, M. K. Tripathi, S. Tiwari, Ruchi Asati, Niraj Tripathi, R. S. Sikarwar","doi":"10.18805/lr-5327","DOIUrl":"https://doi.org/10.18805/lr-5327","url":null,"abstract":"Background: Cicer arietinum (L.), is a legume being grown world wide as a good source of vegan protein. It is a vital part of human feed. Quantification of biochemical parameters of seed is one of the requirements for breeding programmes to develop cultivars suitable for human consumption and food industry. The objective of the present investigation was to evaluate chickpea genotypes on the basis of biochemical parameters to select superior germplasm for further crop improvement. Methods: In this investigation, seventy-one chickpea genotypes employed for different biochemical parameters analysis including protein, total free amino acid and sugar content, reducing and non-reducing sugar, phytic acid, total phenol, flavonoid and tannin content along with DPPH radical scavenging activities. Result: Genotypes showed substantial variation for different biochemical parameters. Maximum seed protein content was found in genotype JG315 (25.1%) and lowest in SAGL-152344 (14.3%), whilst content of amino acid ranged between 2.4 mg/g (SAGL-152318) to 9.51 mg/g (SAGL-152330). Maximum phytic acid content was evident in genotype SAGL22-122 (20.7 mg/g) and lowest in JG315 (4.78 mg/g). Range of total phenol content varied between 0.72mg/g (RVSSG 92) to 1.91 mg/g (ICCV20116).\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":" 55","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825468","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: Cultivation of cereal followed by legume on the same land not only provide essential dietary carbohydrate, protein and vitamin to household but also enhances soil fertility through the symbiotic process of nitrogen fixation by legume. Moreover, legume contribute quality crop residues suitable for livestock feed. Beyond these nutritional and soil health benefits, adopting such farming practices has the potential to boost the income and livelihoods of smallholder farmers. The concurrent use of organic manure and inorganic fertilizer not only mitigates soil hazards but also significantly enhances crop productivity. Methods: A field experiment was conducted at wetland farms, Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, India during summer-kharif seasons of the year 2022 and navarai - summer seasons of the year 2022-23 on aerobic rice-blackgram cropping system. Result: The positive influence of legumes on soil quality is considered a crucial factor in enhancing the production of non-legume crops grown in a rotation system. The success of a cropping system relies on the judicious management of resources, with a particular emphasis on the balanced utilization of organic manures, inorganic fertilizers and biofertilizer. The present study concluded that residual effect of 50% RDN through inorganic fertilizer + 25% of RDN through enriched FYM + 25% RDN through vermicompost + AM fungi + foliar application of nutrients (0.5% Urea + 1% FeSO4 + 0.5% ZnSO4 at 25 and 45 DAS) (S6) recorded significantly higher growth parameters, yield parameters and yield of succeeding blackgram compared to other sources of nutrients.
{"title":"Assessment of Correlation between Growth and Yield of Blackgram with Residual Effect of Organic and Inorganic Sources of Nutrients and AM Fungi Applied to Rice in Aerobic Rice-blackgram Cropping System","authors":"A. Sangothari, S. Radhamani","doi":"10.18805/lr-5336","DOIUrl":"https://doi.org/10.18805/lr-5336","url":null,"abstract":"Background: Cultivation of cereal followed by legume on the same land not only provide essential dietary carbohydrate, protein and vitamin to household but also enhances soil fertility through the symbiotic process of nitrogen fixation by legume. Moreover, legume contribute quality crop residues suitable for livestock feed. Beyond these nutritional and soil health benefits, adopting such farming practices has the potential to boost the income and livelihoods of smallholder farmers. The concurrent use of organic manure and inorganic fertilizer not only mitigates soil hazards but also significantly enhances crop productivity. Methods: A field experiment was conducted at wetland farms, Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, India during summer-kharif seasons of the year 2022 and navarai - summer seasons of the year 2022-23 on aerobic rice-blackgram cropping system. Result: The positive influence of legumes on soil quality is considered a crucial factor in enhancing the production of non-legume crops grown in a rotation system. The success of a cropping system relies on the judicious management of resources, with a particular emphasis on the balanced utilization of organic manures, inorganic fertilizers and biofertilizer. The present study concluded that residual effect of 50% RDN through inorganic fertilizer + 25% of RDN through enriched FYM + 25% RDN through vermicompost + AM fungi + foliar application of nutrients (0.5% Urea + 1% FeSO4 + 0.5% ZnSO4 at 25 and 45 DAS) (S6) recorded significantly higher growth parameters, yield parameters and yield of succeeding blackgram compared to other sources of nutrients.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825982","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}
S. Ravi, S.R. Shri Rangasami, K. Sathiya, V. Rajanbabu, S. A. Fanish, R. Murugaragavan, G. Yazhini, T. Pradeesh Kumar
Background: The precise application of phosphorus fertilizer is pivotal in determining Groundnut (Arachis hypogaea L.) productivity. The high demand of phosphorus for energy transfer molecules involved in nitrogen fixation makes it essential for leguminous crop. A field experiment was conducted at Perunthalaivar Kamaraj Krishi Vigyan Kendra, Puducherry during 2021 to 2023 to explore the performance of groundnut by employing an optimal combination of microbial culture alongside varying levels of phosphorus. Methods: The experiment was laid out in randomized block design with 10 treatments and replicated thrice. The treatments consisted of different doses of phosphorus (@ 20, 40 and 60 kg/ha) with and without seed treatment with DGRC culture. Result: The experiment results of the three years study revealed that a variable response of groundnut to P fertilizer rates and rhizobium inoculant on sandy loam soil in puducherry. P fertilizer rates combined with DGRC inoculant had a significant influence on growth, root nodule, nodule dry weight, pod and kernel yield. From this study, it may concluded that combined application of P fertilizer @ 60 kg/ha and seed treatment of DGRC culture inoculants @ 20 g/kg seed have the potential to increase the productivity and profitability of groundnut. Correlation and Regression analysis also indicated that the yield attributes had a positive impact on groundnut yield.
{"title":"Numerical Variables Analysis and Improving Phosphorus Use Efficiency in Groundnut with Microbial Cultures in Coastal Zone of Puducherry","authors":"S. Ravi, S.R. Shri Rangasami, K. Sathiya, V. Rajanbabu, S. A. Fanish, R. Murugaragavan, G. Yazhini, T. Pradeesh Kumar","doi":"10.18805/lr-5351","DOIUrl":"https://doi.org/10.18805/lr-5351","url":null,"abstract":"Background: The precise application of phosphorus fertilizer is pivotal in determining Groundnut (Arachis hypogaea L.) productivity. The high demand of phosphorus for energy transfer molecules involved in nitrogen fixation makes it essential for leguminous crop. A field experiment was conducted at Perunthalaivar Kamaraj Krishi Vigyan Kendra, Puducherry during 2021 to 2023 to explore the performance of groundnut by employing an optimal combination of microbial culture alongside varying levels of phosphorus. Methods: The experiment was laid out in randomized block design with 10 treatments and replicated thrice. The treatments consisted of different doses of phosphorus (@ 20, 40 and 60 kg/ha) with and without seed treatment with DGRC culture. Result: The experiment results of the three years study revealed that a variable response of groundnut to P fertilizer rates and rhizobium inoculant on sandy loam soil in puducherry. P fertilizer rates combined with DGRC inoculant had a significant influence on growth, root nodule, nodule dry weight, pod and kernel yield. From this study, it may concluded that combined application of P fertilizer @ 60 kg/ha and seed treatment of DGRC culture inoculants @ 20 g/kg seed have the potential to increase the productivity and profitability of groundnut. Correlation and Regression analysis also indicated that the yield attributes had a positive impact on groundnut yield.\u0000","PeriodicalId":17998,"journal":{"name":"LEGUME RESEARCH - AN INTERNATIONAL JOURNAL","volume":"12 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641161","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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