Efficient Harvest Prediction in Agriculture using Machine Learning Techniques

IF 0.4 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Data Mining Modelling and Management Pub Date : 2022-08-09 DOI:10.46610/jodmm.2022.v07i02.005
S. V, Rohit J Kashyap, R. Oommen, D. ., Bhoomika ., R. Swathi
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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.
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利用机器学习技术进行农业高效收成预测
给定的系统包括印度各邦的庞大数据集,但在之前的系统中,只有一个邦被选中。所有的农民都将通过使用图画来更好地了解要种植的作物。机器学习的特征给出了信息的详细结构,并给出了预测。主要解决作物预测、轮作技术、水分利用、肥料需求、安全等问题。由于周围气候的变化,需要有熟练的技术来发展作物,并帮助农民掌握生产和管理特点的知识。该项目通过选择土地进行耕作,为先进的农业技术提供了适当的结果,这可以帮助农民获得大量的知识。
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来源期刊
International Journal of Data Mining Modelling and Management
International Journal of Data Mining Modelling and Management COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
1.10
自引率
0.00%
发文量
22
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
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