Statistical Growth Prediction Analysis of Rice Crop with Pixel-Based
Mapping Technique
Abstract
Agriculture has attracted eminent researchers during the past few
decades owing to revolutionary advancements in the field of data
analysis using machine learning and computer vision techniques. The
continuous monitoring of plant growth is an important aspect in the
field of agriculture and has associated challenges also. The current
work aims to define the significance of the pixel-based clustering
techniques for analyzing plant growth in terms of height calculation. In
the proposed work, pixel-based mapping has implemented its two
applications viz. vertical and horizontal scaling for height
calculation. Here, vertical mapping implements an image processing
technique to monitor the height of a single plant whereas the horizontal
mapping technique determines the average volume of the whole field using
k-means. During the result analysis, it is observed that the proposed
model provides an accuracy of 97.58% outperforming the state-of-the-art
models. Another exciting characteristic of the proposed model is that it
is hardware-free which further escalates the scope of its implementation
in a real-life scenario.