Application of Machine Learning Techniques for Yield Prediction on Delineated Zones in Precision Agriculture

Anshal Savla, Himtanaya Bhadada, Parul Dhawan, V. Joshi
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引用次数: 6

Abstract

Precision agriculture is the implementation of the recent technology in agriculture. Huge amount of data is collected in agriculture and various techniques of data mining are used to make efficient use of it. In this paper, we have discussed how with the help of both, clustering and classification algorithms, the crop suitable for a particular piece of land can be determined. Management zone delineation is a key task in this. From a data-mining point of view this comes down to variant of spatial clustering which has a constraint of keeping the resulting clusters spatially mostly contiguous. We analyze the need to discretize and normalize the data set and the various techniques that are used for the same. Further, a comparative analysis of the algorithm is shown where it can be seen which algorithm is best suited. We also talk about the future scope of the same and how these could actually be implemented in the real life scenarios.
机器学习技术在精准农业圈定区产量预测中的应用
精准农业是现代农业技术在农业中的应用。农业中收集了大量的数据,需要使用各种数据挖掘技术对其进行有效利用。在本文中,我们讨论了如何在聚类和分类算法的帮助下,确定适合特定土地的作物。管理区划定是其中的一项关键任务。从数据挖掘的角度来看,这可以归结为空间聚类的变体,它具有保持结果聚类在空间上大部分连续的约束。我们分析了离散化和规范化数据集的需要,以及用于同一数据集的各种技术。此外,对算法进行比较分析,可以看出哪种算法最适合。我们还讨论了相同的未来范围以及如何在现实生活场景中实际实现这些。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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