Statistical growth prediction analysis of rice crop with pixel-based mapping technique. (7th October 2022)

• Record Type: Journal Article
• Authors: Mangla, Monika; Mehta, Vaishali; Mohanty, Sachi Nandan; Sharma, Nonita; Preetham, Anusha
• Is Part Of: International journal of artificial intelligence and soft computing. Volume 7:Number 3(2022)
• Page Start: 208
• Page End: 227
• Publication Date: 2022-10-07
• Subjects: image processing -- pixel based mapping -- leaf growth analysis -- scaling -- machine learning -- k-means clustering; Artificial intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-4950

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.