{"title":"预测分析在农业中的应用","authors":"Dr. Shashi Kant Gupta","doi":"10.36647/ttidmkd/02.04.a001","DOIUrl":null,"url":null,"abstract":"Predictive analytics is one the most helpful techniques in business which provides the opportunity to forecast the upcoming risks and challenges in business. The assistance of predictive analytics provides the scope towards an enterprise to perform business with more effective strategies and decisions to avail higher success rate in business surroundings. As the competition in agriculture has been increasing day by day, business companies in the agricultural sector have started to implement predictive analytics in their operational area. It helps to identify upcoming issues and take steps to mitigate those risk factors in business. The study has been focused on analyzing the impact of predictive analytics in agriculture. Thus, various concepts and theories related with predictive analytics have been discussed in detailed manner within the entire study. An inductive approach has been taken while performing the entire task, cross sectional design has been used to perform the study effectively. On the other hand, qualitative data has been collected using secondary data collection methods within this particular study. The entire study has found that the application of predictive analytics mostly helps to prepare to face upcoming challenges and gain higher consumer satisfaction, competitive advantage and superior business growth in the agricultural industry throughout the global periphery.","PeriodicalId":314032,"journal":{"name":"TechnoareteTransactions on Intelligent Data Mining and Knowledge Discovery","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Predictive Analytics in Agriculture\",\"authors\":\"Dr. Shashi Kant Gupta\",\"doi\":\"10.36647/ttidmkd/02.04.a001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predictive analytics is one the most helpful techniques in business which provides the opportunity to forecast the upcoming risks and challenges in business. The assistance of predictive analytics provides the scope towards an enterprise to perform business with more effective strategies and decisions to avail higher success rate in business surroundings. As the competition in agriculture has been increasing day by day, business companies in the agricultural sector have started to implement predictive analytics in their operational area. It helps to identify upcoming issues and take steps to mitigate those risk factors in business. The study has been focused on analyzing the impact of predictive analytics in agriculture. Thus, various concepts and theories related with predictive analytics have been discussed in detailed manner within the entire study. An inductive approach has been taken while performing the entire task, cross sectional design has been used to perform the study effectively. On the other hand, qualitative data has been collected using secondary data collection methods within this particular study. The entire study has found that the application of predictive analytics mostly helps to prepare to face upcoming challenges and gain higher consumer satisfaction, competitive advantage and superior business growth in the agricultural industry throughout the global periphery.\",\"PeriodicalId\":314032,\"journal\":{\"name\":\"TechnoareteTransactions on Intelligent Data Mining and Knowledge Discovery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"TechnoareteTransactions on Intelligent Data Mining and Knowledge Discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36647/ttidmkd/02.04.a001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"TechnoareteTransactions on Intelligent Data Mining and Knowledge Discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36647/ttidmkd/02.04.a001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Predictive Analytics in Agriculture
Predictive analytics is one the most helpful techniques in business which provides the opportunity to forecast the upcoming risks and challenges in business. The assistance of predictive analytics provides the scope towards an enterprise to perform business with more effective strategies and decisions to avail higher success rate in business surroundings. As the competition in agriculture has been increasing day by day, business companies in the agricultural sector have started to implement predictive analytics in their operational area. It helps to identify upcoming issues and take steps to mitigate those risk factors in business. The study has been focused on analyzing the impact of predictive analytics in agriculture. Thus, various concepts and theories related with predictive analytics have been discussed in detailed manner within the entire study. An inductive approach has been taken while performing the entire task, cross sectional design has been used to perform the study effectively. On the other hand, qualitative data has been collected using secondary data collection methods within this particular study. The entire study has found that the application of predictive analytics mostly helps to prepare to face upcoming challenges and gain higher consumer satisfaction, competitive advantage and superior business growth in the agricultural industry throughout the global periphery.