使用机器学习技术预测作物类型的产量和给定地块的需水量

Nitin Padriya, Nimisha Patel
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引用次数: 0

摘要

物联网(IoT)智能技术使新型数字农业成为可能。技术已经成为应对当今挑战的必要条件,许多行业正在使用最新技术实现流程自动化。通过最大限度地使用肥料来提高植物效率,基于物联网技术的智能农业旨在帮助生产者和农民减少浪费,同时提高产量。有了基于物联网的智能农业,农民可以更好地管理他们的动物,开发作物,节省成本,节约资源。气候监测、干旱探测、农业和生产、污染分布以及更多的应用都依赖于天气预报。预报的准确性是由以前的天气条件在大范围和长时间内决定的。机器学习算法可以帮助我们建立一个具有适当精度的模型。因此,在有限的种植面积上提高产量是很重要的。物联网智能农业是一种高科技方法,可以让人们以清洁和可持续的方式种植作物。在农业方面,它是对当前信息和通信技术的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting yield of crop type and water requirement for a given plot of land using machine learning techniques
Internet of things (IoT) smart technology enables new digital agriculture. Technology has become necessary to address today's challenges, and many sectors are automating their processes with the newest technologies. By maximizing fertiliser use to boost plant efficiency, smart agriculture, which is based on IoT technology, intends to assist producers and farmers in reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly and sustainably. In agriculture, it is the use of current information and communication technologies.
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