基于自动像素映射技术的恰蒂斯加尔邦水稻统计生长分析

IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
B. Patel, Aakanksha Sharaff, S. Verulkar
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引用次数: 0

摘要

田间作物的统计生长分析已成为农业中的一大挑战。通过自动化分析作物的生长情况,对于农民了解植物因生长监测不规律而出现的问题具有广泛的意义。这项工作背后的思想是,从水稻作物高度计算的角度来看,利用基于像素的聚类技术进行映射对生长分析的重要性(水稻品种为MTU-1010)。高度测量在健康作物的定期评估中起着至关重要的作用,本工作中提出的方法在来自恰蒂斯加尔邦赖布尔英迪拉·甘地农业大学的14个采样数据集中实现了97.58%的准确率;已经准备了实时数据集。所提出的工作用于分析垂直和水平缩放技术。垂直映射提供单个植物的高度,而使用k均值聚类的水平映射提供整个田地的平均高度。这项工作使用机器学习,图像处理技术用于这项工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical Growth Analysis of Rice Plants in Chhattisgarh Region Using Automated Pixel-Based Mapping Technique
The statistical growth analysis of field crop has become a great challenge in agriculture. Analyzing the growth of crop through automation provides extensive significance to the farmers for getting information about the problem arising in plants due to irregular growth monitoring. The idea behind this work is the importance of mapping with pixel-based clustering technique for growth analysis in terms of height calculation of rice crop (rice variety is MTU-1010). Height measurement plays a vital role in regular assessment for a healthy crop, and the approach proposed in this work achieves 97.58% accuracy of 14 sampled datasets taken from Indira Gandhi Agriculture University of Raipur, Chhattisgarh; a real-time dataset has been prepared. Proposed work is used for analyzing vertical as well as horizontal scaling technique. Vertical mapping provides the height of a single plant whereas horizontal mapping using k-means clustering provides an average height of the whole field. This work uses machine learning, and image processing techniques are used for this work.
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来源期刊
International Journal of System Dynamics Applications
International Journal of System Dynamics Applications COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
自引率
38.90%
发文量
26
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