Pub Date : 2021-07-13DOI: 10.47059/ALINTERI/V36I2/AJAS21110
M. Shyamsunder, V. Mohan
The yield of agriculture primarily depends on the soil moisture, soil fertility and the use of suitable fertilizers. The method of manually measuring the soil nutrients is inaccurate in the current scenario due to laps between soil samples collected at the field and measuring in the laboratory. IoT has made changes in so many fields to monitor the data remotely despite of existing wireless technologies like Zigbee, GSM, etc. In this work an effort is made to collect the data related to various soil nutrients from agriculture filed using multiple sensors. Once the data is monitored and collected at the control center helps to apply a machine algorithms to take the appropriate decision for an efficient crop yield. In the proposed system, the sensors connected to the node at the field measures the macro nutrients of the soil, temperature and humidity of the soil. The nutrition majorly required for the growth of the plant is nitrogen (N), potassium (K), and phosphorous (P) amount present in the soil. In this work a microcontroller with WiFi is used to interface various sensors and display the measured value in the LCD. This application will provide a user interface to monitor the fertilizers, irrigation and humidity control.
{"title":"IoT based Soil Quality Monitoring for An Efficient Irrigation","authors":"M. Shyamsunder, V. Mohan","doi":"10.47059/ALINTERI/V36I2/AJAS21110","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I2/AJAS21110","url":null,"abstract":"The yield of agriculture primarily depends on the soil moisture, soil fertility and the use of suitable fertilizers. The method of manually measuring the soil nutrients is inaccurate in the current scenario due to laps between soil samples collected at the field and measuring in the laboratory. IoT has made changes in so many fields to monitor the data remotely despite of existing wireless technologies like Zigbee, GSM, etc. In this work an effort is made to collect the data related to various soil nutrients from agriculture filed using multiple sensors. Once the data is monitored and collected at the control center helps to apply a machine algorithms to take the appropriate decision for an efficient crop yield. In the proposed system, the sensors connected to the node at the field measures the macro nutrients of the soil, temperature and humidity of the soil. The nutrition majorly required for the growth of the plant is nitrogen (N), potassium (K), and phosphorous (P) amount present in the soil. In this work a microcontroller with WiFi is used to interface various sensors and display the measured value in the LCD. This application will provide a user interface to monitor the fertilizers, irrigation and humidity control.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88045695","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}
Pub Date : 2021-07-13DOI: 10.47059/ALINTERI/V36I2/AJAS21112
Z. Khan, M. Khubrani, Shadab Alam, S. Hui, Yuge Wang
The automatic classification of historical data of myriad diverse meteorological sequences in the annual period can help to find the climate differences through key phenology of rice. In this paper, a hybrid gradients-shape dynamic time warping (HGSDTW) algorithm is proposed to measure the similarity of meteorological data during the diverse rice growth period at various locations. The weighting calculation of Euclidean distance uses the form factor in the rice jointing and heading stage. The distance matrix constructs first & second-level gradient single-factor transformation sequences during the period. The dynamic programming method obtains the similarity distances of single and multiple meteorological factors. The results show that the classification accuracy rate from HGSDTW of the heading & jointing stage is higher than that of other similar algorithms. Furthermore, it can observe that the clustering number increases the classification accuracy, and the HGSDTW algorithm maintains the accuracy of 14% for varieties of rice at diverse locations to multiple years of jointing. Besides, the automatic classification experiment of sequence period shows that the classification accuracy of this method is higher than that of another similarity measure. The classification accuracy rate of the heading stage sequence is 10%~14% higher than that of a similar previous standard measurement algorithm, and the jointing period is 1%~9% higher. In this case, the cluster number increasing the classification accuracy, and the HGSDTW maintain the overall accuracy of 14%. Thus, this method can be effectively combined with the classification algorithm to improve the efficiency of the automatic classification of multi-weather sequence data in key phenological periods of rice.
{"title":"Method for Measuring the Similarity of Multiple Metrological Sequences in the Key Phenological Phase of Rice-based on Dynamic Time","authors":"Z. Khan, M. Khubrani, Shadab Alam, S. Hui, Yuge Wang","doi":"10.47059/ALINTERI/V36I2/AJAS21112","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I2/AJAS21112","url":null,"abstract":"The automatic classification of historical data of myriad diverse meteorological sequences in the annual period can help to find the climate differences through key phenology of rice. In this paper, a hybrid gradients-shape dynamic time warping (HGSDTW) algorithm is proposed to measure the similarity of meteorological data during the diverse rice growth period at various locations. The weighting calculation of Euclidean distance uses the form factor in the rice jointing and heading stage. The distance matrix constructs first & second-level gradient single-factor transformation sequences during the period. The dynamic programming method obtains the similarity distances of single and multiple meteorological factors. The results show that the classification accuracy rate from HGSDTW of the heading & jointing stage is higher than that of other similar algorithms. Furthermore, it can observe that the clustering number increases the classification accuracy, and the HGSDTW algorithm maintains the accuracy of 14% for varieties of rice at diverse locations to multiple years of jointing. Besides, the automatic classification experiment of sequence period shows that the classification accuracy of this method is higher than that of another similarity measure. The classification accuracy rate of the heading stage sequence is 10%~14% higher than that of a similar previous standard measurement algorithm, and the jointing period is 1%~9% higher. In this case, the cluster number increasing the classification accuracy, and the HGSDTW maintain the overall accuracy of 14%. Thus, this method can be effectively combined with the classification algorithm to improve the efficiency of the automatic classification of multi-weather sequence data in key phenological periods of rice.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82305019","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}
Pub Date : 2021-07-13DOI: 10.47059/ALINTERI/V36I2/AJAS21108
C. Waghmare, Prajakta R Pazare, K. Ansari
Water is the vital natural resource for the survival all biotic species. Demand of water is growing day by day as a result of rapid industrialization, production, and growth in population. As a result, it is necessary to look for the alternatives to reduce our freshwater usage. Grey-water treatment appears to be one of the most promising alternatives. The conventional filtration process with sand as a filter media is considered as a cost effective technique for water and waste water treatment. Amongst the various techniques of filtration, the performance of the Multicompartment Sand Filter, a modified version of a sand filter is examined in this paper in four different experimental setups. It is discovered that this sand filter is effective in removing Chemical Oxygen Demand, Total Suspended Solids and turbidity with percentage removal of 95.94%, 89.72%% and 64.69%% respectively. This filter is easy to manage, adaptable, compact and cost effective.
{"title":"A Preliminary Investigation on Performance of Multicompartment Sand Filter for Treatment of Grey-Water","authors":"C. Waghmare, Prajakta R Pazare, K. Ansari","doi":"10.47059/ALINTERI/V36I2/AJAS21108","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I2/AJAS21108","url":null,"abstract":"Water is the vital natural resource for the survival all biotic species. Demand of water is growing day by day as a result of rapid industrialization, production, and growth in population. As a result, it is necessary to look for the alternatives to reduce our freshwater usage. Grey-water treatment appears to be one of the most promising alternatives. The conventional filtration process with sand as a filter media is considered as a cost effective technique for water and waste water treatment. Amongst the various techniques of filtration, the performance of the Multicompartment Sand Filter, a modified version of a sand filter is examined in this paper in four different experimental setups. It is discovered that this sand filter is effective in removing Chemical Oxygen Demand, Total Suspended Solids and turbidity with percentage removal of 95.94%, 89.72%% and 64.69%% respectively. This filter is easy to manage, adaptable, compact and cost effective.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75691023","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}
Pub Date : 2021-07-13DOI: 10.47059/ALINTERI/V36I2/AJAS21115
Ezgi Gur
Medicinal Sage is consumed as tea in sore throat and kidney diseases caused by cold and flu. It also has sedative, diuretic, antiperspirant and disinfectant effects. Thujone, which is found in the essential oil of Salvia officinalis species, is an essential oil component with very strong antiseptic and antibiotic effects. Sage (Salvia officinalis), which is a medicinal and an aromatic plant and has a wide area of usage, is cultivated due to these properties. However, the most critical cost item in the production of sage is the weeding done in the first years. The understory weeding done without using herbicides continues until the sage seedlings shield the soil and prevent the development of other herbs. The aim of this research was to determine the effects of hormone treatment on germination success and seedling morphological characters in sage seeds. Within the scope of this research, sage seeds were planted by being exposed to IAA, IBA, GA3 and NAA hormones at 1000, 2500 and 5000 ppm concentrations for 3 to 5 seconds and at 50, 100 and 200 ppm doses for 24 hours, and thus 26 applications were performed together with the control groups. The seeds were planted in sterile peat medium after the hormone treatments, and the effect of hormone treatments on the germination percentage and some seedling characters was tried to be found after 30 days of germination. As a result of the research, it was found that the hormone treatments positively affected most of seedling characters.
{"title":"The Effect of Hormone Treatments on Germination and Seedling Characters of Sage (Salvia officinalis L.) Seeds","authors":"Ezgi Gur","doi":"10.47059/ALINTERI/V36I2/AJAS21115","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I2/AJAS21115","url":null,"abstract":"Medicinal Sage is consumed as tea in sore throat and kidney diseases caused by cold and flu. It also has sedative, diuretic, antiperspirant and disinfectant effects. Thujone, which is found in the essential oil of Salvia officinalis species, is an essential oil component with very strong antiseptic and antibiotic effects. Sage (Salvia officinalis), which is a medicinal and an aromatic plant and has a wide area of usage, is cultivated due to these properties. However, the most critical cost item in the production of sage is the weeding done in the first years. The understory weeding done without using herbicides continues until the sage seedlings shield the soil and prevent the development of other herbs. The aim of this research was to determine the effects of hormone treatment on germination success and seedling morphological characters in sage seeds. Within the scope of this research, sage seeds were planted by being exposed to IAA, IBA, GA3 and NAA hormones at 1000, 2500 and 5000 ppm concentrations for 3 to 5 seconds and at 50, 100 and 200 ppm doses for 24 hours, and thus 26 applications were performed together with the control groups. The seeds were planted in sterile peat medium after the hormone treatments, and the effect of hormone treatments on the germination percentage and some seedling characters was tried to be found after 30 days of germination. As a result of the research, it was found that the hormone treatments positively affected most of seedling characters.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73618048","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}
Pub Date : 2021-07-13DOI: 10.47059/ALINTERI/V36I2/AJAS21118
Dr.B. Rama Subba Reddy, D. G. Madhavi, C. H. S. Lakshmi, Dr.K. Venkata Nagendra, DR. R. Sri̇devi̇
Agriculture is vital to the Indian economy as over 17 percent of total GDP and employs more than 60 percent of the population relies on agriculture. This research focuses on plant diseases as they create a major threat to food production as well as for small-scale farmer’s livelihood. Expert workers are employed in traditional farming to visually evaluate row by row to identify plant diseases, which is a time-consuming, labor-intensive activity that is potentially error-prone because it is done by humans. The aim of this research is to develop an automated detection model that uses a combination of image processing and deep learning techniques (Faster R-CNN+ResNet50) to analyze real-time images and detect and classify the three common maize plant diseases: Common Rust, Cercospora Leaf Spot, and Northern Leaf Blight. The proposed system achieved 91% accuracy and successfully detects three maize diseases.
{"title":"Detection of Disease in Maize Plant Using Deep Learning","authors":"Dr.B. Rama Subba Reddy, D. G. Madhavi, C. H. S. Lakshmi, Dr.K. Venkata Nagendra, DR. R. Sri̇devi̇","doi":"10.47059/ALINTERI/V36I2/AJAS21118","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I2/AJAS21118","url":null,"abstract":"Agriculture is vital to the Indian economy as over 17 percent of total GDP and employs more than 60 percent of the population relies on agriculture. This research focuses on plant diseases as they create a major threat to food production as well as for small-scale farmer’s livelihood. Expert workers are employed in traditional farming to visually evaluate row by row to identify plant diseases, which is a time-consuming, labor-intensive activity that is potentially error-prone because it is done by humans. The aim of this research is to develop an automated detection model that uses a combination of image processing and deep learning techniques (Faster R-CNN+ResNet50) to analyze real-time images and detect and classify the three common maize plant diseases: Common Rust, Cercospora Leaf Spot, and Northern Leaf Blight. The proposed system achieved 91% accuracy and successfully detects three maize diseases.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89488470","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}
Pub Date : 2021-07-13DOI: 10.47059/ALINTERI/V36I2/AJAS21111
Balakrishnan Natarajan, Dr.A. Vanitha
In image processing, the radical scheme is required to propose a model for extracting the required content from an image. It plays a critical position to offer significant facts and needs methods in various automation arenas. By keeping the way of a parting textual content from images has proposed via following the sparse matrix illustration, grouping text components are based on heuristic rules and clustered into sentence generation. This paper directs a study on image analysis that inspects visual items as objects and different text patterns. Logistic Regression, Linear Discriminant Analysis naïve Bayes Algorithm are used to predict the image forms. This proposed work promotes the learning algorithm called Learning Vector Quantization Prediction Algorithm (LVQ Predict) is used to analysis the parts of the image. The features are extracted and classifies into printed and non-printed texts. Further, these texts are normalized and documented.
{"title":"A Comprehensive Study on Intelligence System for Automatize Event Tracker System Using Learning Method","authors":"Balakrishnan Natarajan, Dr.A. Vanitha","doi":"10.47059/ALINTERI/V36I2/AJAS21111","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I2/AJAS21111","url":null,"abstract":"In image processing, the radical scheme is required to propose a model for extracting the required content from an image. It plays a critical position to offer significant facts and needs methods in various automation arenas. By keeping the way of a parting textual content from images has proposed via following the sparse matrix illustration, grouping text components are based on heuristic rules and clustered into sentence generation. This paper directs a study on image analysis that inspects visual items as objects and different text patterns. Logistic Regression, Linear Discriminant Analysis naïve Bayes Algorithm are used to predict the image forms. This proposed work promotes the learning algorithm called Learning Vector Quantization Prediction Algorithm (LVQ Predict) is used to analysis the parts of the image. The features are extracted and classifies into printed and non-printed texts. Further, these texts are normalized and documented.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82919545","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}
Pub Date : 2021-07-13DOI: 10.47059/ALINTERI/V36I2/AJAS21114
A. Dmytro, Sviren Mykola, Onopa Volodymyr, Deikun Viktor, Majara Vitaliy
A formation of a seedbed is an important step during seed sowing process. A quality of seedbed formation influences on seeds distribution along both a row and a depth and is triggering the opportunity to obtain early and even sprouts. The design of the furrow opener is the main element that has a direct impact on the qualitative formation of seedbed and technological parameters of coulter operation. During the research, there has been analyzed the modern construction of precision seed drills coulters and specified advantages and disadvantages of their operation. It has been established that the most advanced are coulters having a working section with a combined angle (sharp and obtuse) of entry into the soil. The attained results afforded to develop an improved design of the coulter furrow opener of the precision seed drill. There was brought forward a combined wedge furrow opener, the upper part of which has a working section with a sharp angle of entry into the soil, lower - and compactor, located in the rear part of the furrow opener, which forms seedbed has a working surface with an obtuse angle of entry into the soil. There were obtained analytical dependences targeted to determine the main structural and technological parameters of the operating elements of a combined coulter furrow opener which is used to seed cultivated crops: the angles of entry into the soil of the upper and lower part of the furrow opener, compactor in the rolling plane and the angle of tip of the furrow opener in the horizontal plane.
{"title":"Grounding of Design and Technology Parameters of Combined Coulter Furrow Opener of Precision Seed Drill","authors":"A. Dmytro, Sviren Mykola, Onopa Volodymyr, Deikun Viktor, Majara Vitaliy","doi":"10.47059/ALINTERI/V36I2/AJAS21114","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I2/AJAS21114","url":null,"abstract":"A formation of a seedbed is an important step during seed sowing process. A quality of seedbed formation influences on seeds distribution along both a row and a depth and is triggering the opportunity to obtain early and even sprouts. The design of the furrow opener is the main element that has a direct impact on the qualitative formation of seedbed and technological parameters of coulter operation. During the research, there has been analyzed the modern construction of precision seed drills coulters and specified advantages and disadvantages of their operation. It has been established that the most advanced are coulters having a working section with a combined angle (sharp and obtuse) of entry into the soil. The attained results afforded to develop an improved design of the coulter furrow opener of the precision seed drill. There was brought forward a combined wedge furrow opener, the upper part of which has a working section with a sharp angle of entry into the soil, lower - and compactor, located in the rear part of the furrow opener, which forms seedbed has a working surface with an obtuse angle of entry into the soil. There were obtained analytical dependences targeted to determine the main structural and technological parameters of the operating elements of a combined coulter furrow opener which is used to seed cultivated crops: the angles of entry into the soil of the upper and lower part of the furrow opener, compactor in the rolling plane and the angle of tip of the furrow opener in the horizontal plane.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76018800","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}
Pub Date : 2021-07-13DOI: 10.47059/ALINTERI/V36I2/AJAS21119
Jahangeer Mohd Reshi, J. Sharma, I. A. Najar
The current study was conducted over a two-year study period at Manasbal Lake, which has a catchment area of 22 km2 and is located in the district of Ganderbal, 30 kilometers north of the city of Srinagar in Jammu and Kashmir. The Manasbal catchment is defined by latitudes 34°14' - 34°16' N and longitudes 74°40' - 74°43' E, with an elevation of approximately 1551m a.s.l. During the present study, 101 phytoplankton species from six groups were identified from Manasbal Lake: Bacillariophyceae, Chlorophyceae, Cyanophyceae, Chrysophyceae, Dinophyceae, and Euglenophyceae. Among these, Bacillariophyceae formed the bulk of phytoplankton with 49 species, followed by Chlorophyceae (39), Cyanophyceae (7), Euglenophyceae (3), Dinophyceae (2) and Chrysophyceae (1). The Bacillariophyceae, the dominant group, was present at all six sites with the maximum diversity of species.
{"title":"Taxonomic Survey of Phytoplankton in Manasbal Lake of Kashmir Himalaya, India","authors":"Jahangeer Mohd Reshi, J. Sharma, I. A. Najar","doi":"10.47059/ALINTERI/V36I2/AJAS21119","DOIUrl":"https://doi.org/10.47059/ALINTERI/V36I2/AJAS21119","url":null,"abstract":"The current study was conducted over a two-year study period at Manasbal Lake, which has a catchment area of 22 km2 and is located in the district of Ganderbal, 30 kilometers north of the city of Srinagar in Jammu and Kashmir. The Manasbal catchment is defined by latitudes 34°14' - 34°16' N and longitudes 74°40' - 74°43' E, with an elevation of approximately 1551m a.s.l. During the present study, 101 phytoplankton species from six groups were identified from Manasbal Lake: Bacillariophyceae, Chlorophyceae, Cyanophyceae, Chrysophyceae, Dinophyceae, and Euglenophyceae. Among these, Bacillariophyceae formed the bulk of phytoplankton with 49 species, followed by Chlorophyceae (39), Cyanophyceae (7), Euglenophyceae (3), Dinophyceae (2) and Chrysophyceae (1). The Bacillariophyceae, the dominant group, was present at all six sites with the maximum diversity of species.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80060933","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}
Pub Date : 2021-06-29DOI: 10.47059/alinteri/v36i1/ajas21105
Dr. K. Sudhakar, L. Amalraj, V. Tejaswini, N. M. Sree, P. Harshitha, M. Julie
A polymer is a material which consists of very large molecules, or macromolecules, composed of many repeating subunits. They are classified as synthetic and natural polymers both play essential roles in everyday life due to their broad spectrum of properties. The foremost important class of polymers is superabsorbent polymer (SAP) materials. They are hydrophilic networks which absorb and retain large amounts of water. SAPs are originally divided into two main classes. They are Synthetic (Petrochemical based) and Natural (e.g.; Polysaccharide and Polypeptide based). Most of the present superabsorbent polymers are frequently produced from acrylic acid and acrylamide solution or inverse-suspension polymerization techniques. These are not biodegradable and are harmful to the environment that causes pollution. So, we sought to make biodegradable SAP that can act as a fertilizer to improve the soil quality and water conservation in agricultural land.
{"title":"Eco-friendly Biodegradable Super Absorbent Polymers (SAPs); An Effective Water Retainer and Agrofertilizer","authors":"Dr. K. Sudhakar, L. Amalraj, V. Tejaswini, N. M. Sree, P. Harshitha, M. Julie","doi":"10.47059/alinteri/v36i1/ajas21105","DOIUrl":"https://doi.org/10.47059/alinteri/v36i1/ajas21105","url":null,"abstract":"A polymer is a material which consists of very large molecules, or macromolecules, composed of many repeating subunits. They are classified as synthetic and natural polymers both play essential roles in everyday life due to their broad spectrum of properties. The foremost important class of polymers is superabsorbent polymer (SAP) materials. They are hydrophilic networks which absorb and retain large amounts of water. SAPs are originally divided into two main classes. They are Synthetic (Petrochemical based) and Natural (e.g.; Polysaccharide and Polypeptide based). Most of the present superabsorbent polymers are frequently produced from acrylic acid and acrylamide solution or inverse-suspension polymerization techniques. These are not biodegradable and are harmful to the environment that causes pollution. So, we sought to make biodegradable SAP that can act as a fertilizer to improve the soil quality and water conservation in agricultural land.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76511132","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}
Pub Date : 2021-06-29DOI: 10.47059/alinteri/v36i1/ajas21100
Sarah Sameer, P. Sriramya
Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations that break down information for order and relapse investigation. The significance value for calculating Accuracy was found to be 0.005. Result and discussion: During the process of testing 10 iterations have been taken for each of the classification algorithms respectively. The experimental results shows that the decision tree algorithm with mean accuracy of 80.257% is compared with the SVM classifier algorithm of mean accuracy 75.337% Conclusion: Based on the results achieved the Decision Tree classification algorithm better prediction of heart disease than the SVM classifier algorithm.
{"title":"Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease","authors":"Sarah Sameer, P. Sriramya","doi":"10.47059/alinteri/v36i1/ajas21100","DOIUrl":"https://doi.org/10.47059/alinteri/v36i1/ajas21100","url":null,"abstract":"Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations that break down information for order and relapse investigation. The significance value for calculating Accuracy was found to be 0.005. Result and discussion: During the process of testing 10 iterations have been taken for each of the classification algorithms respectively. The experimental results shows that the decision tree algorithm with mean accuracy of 80.257% is compared with the SVM classifier algorithm of mean accuracy 75.337% Conclusion: Based on the results achieved the Decision Tree classification algorithm better prediction of heart disease than the SVM classifier algorithm.","PeriodicalId":42396,"journal":{"name":"Alinteri Journal of Agriculture Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79936612","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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