Title: Statistical growth prediction analysis of rice crop with pixel-based mapping technique

Authors: Monika Mangla; Vaishali Mehta; Sachi Nandan Mohanty; Nonita Sharma; Anusha Preetham

Addresses: Department of Information Technology, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India ' Department of Computer Science and Engineering, Geeta University, Panipat, India ' School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India ' Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi, India ' Department of Artificial Intelligence and Machine Learning, BNM Institute of Technology, Bengaluru, India

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 analysing plant growth in terms of height calculation. In this study, 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.

Keywords: image processing; pixel based mapping; leaf growth analysis; scaling; machine learning; k-means clustering.

DOI: 10.1504/IJAISC.2022.126342

International Journal of Artificial Intelligence and Soft Computing, 2022 Vol.7 No.3, pp.208 - 227

Received: 03 Mar 2022
Received in revised form: 02 Jun 2022
Accepted: 27 Jun 2022
Published online: 21 Oct 2022 *