S. V, Rohit J Kashyap, R. Oommen, D. ., Bhoomika ., R. Swathi
{"title":"Efficient Harvest Prediction in Agriculture using Machine Learning Techniques","authors":"S. V, Rohit J Kashyap, R. Oommen, D. ., Bhoomika ., R. Swathi","doi":"10.46610/jodmm.2022.v07i02.005","DOIUrl":null,"url":null,"abstract":"The given system includes a vast dataset of India's states, but the previous system, only single state was selected. All the farmers will get a better knowledge of the crops to cultivate by using a pictorial depiction. Machine learning features give a detailed structure with the information and it gives the predictions. The main problems like knowing about the crop prediction, rotation techniques, utilization of water, need for fertilizer and safety will be taken care of. Due to varying climatic changes of the surrounding the need to have a proficient techniques are required for development of crops and to help the farmers in their knowledge of production and management features. The project gives the proper results for advanced farming techniques by choosing the land for farming, which can help the farmers to gain huge knowledge about this.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"14 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Mining Modelling and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46610/jodmm.2022.v07i02.005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The given system includes a vast dataset of India's states, but the previous system, only single state was selected. All the farmers will get a better knowledge of the crops to cultivate by using a pictorial depiction. Machine learning features give a detailed structure with the information and it gives the predictions. The main problems like knowing about the crop prediction, rotation techniques, utilization of water, need for fertilizer and safety will be taken care of. Due to varying climatic changes of the surrounding the need to have a proficient techniques are required for development of crops and to help the farmers in their knowledge of production and management features. The project gives the proper results for advanced farming techniques by choosing the land for farming, which can help the farmers to gain huge knowledge about this.
期刊介绍:
Facilitating transformation from data to information to knowledge is paramount for organisations. Companies are flooded with data and conflicting information, but with limited real usable knowledge. However, rarely should a process be looked at from limited angles or in parts. Isolated islands of data mining, modelling and management (DMMM) should be connected. IJDMMM highlightes integration of DMMM, statistics/machine learning/databases, each element of data chain management, types of information, algorithms in software; from data pre-processing to post-processing; between theory and applications. Topics covered include: -Artificial intelligence- Biomedical science- Business analytics/intelligence, process modelling- Computer science, database management systems- Data management, mining, modelling, warehousing- Engineering- Environmental science, environment (ecoinformatics)- Information systems/technology, telecommunications/networking- Management science, operations research, mathematics/statistics- Social sciences- Business/economics, (computational) finance- Healthcare, medicine, pharmaceuticals- (Computational) chemistry, biology (bioinformatics)- Sustainable mobility systems, intelligent transportation systems- National security