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引用次数: 0

摘要

根据Niti Aayog的研究,印度政府计划到2022年使农民的收入翻两番。在另一项研究中,Niti Aayog表示,从2001年到2011年,所有农业活动下降了3.6%。印度及其次大陆的土壤出生率、水量和温度正在发生变化,这是人们放弃农业的主要原因之一。因此,农民的庄稼在收获后不能提供丰富的粮食。该模型将使用朴素贝叶斯方法(一种监督学习技术)来预测最佳作物。与温度、湿度和气候等相关因素一起,植物的种子信息将被真正收集起来,以帮助它们有效和高效地发育。用户可以通过在应用系统上输入诸如面积、天气等参数来预测最适合改良农业的作物。
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A Process for Improved Agriculture: Harvest Forecasting
According to the Niti Aayog study, the Indian government plans to quadruple farmers' incomes by 2022. In different research, Niti Aayog showed that from 2001 to 2011, there was a 3.6 percent decline in all agricultural activity. The soil birthrate, volume of water, and temperature are shifting in India as well as its subcontinent, that is one of the main reasons why people are giving up farming. As a result, the farmers' crops don't provide an abundance following harvest. The naive bayes method, a supervised learning technique, will be used by the model to forecast the best crop. Along with pertinent factors like temperature, humidity, and climate, the seed information for the plants will really be gathered to help them develop effectively and efficiently. Users may anticipate the most appealing crop for improved agriculture by entering parameters like area, weather, etc. on the application system.
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