CALCULABLE-CONTENTS IDENTIFICATION MATRIX: A COMPUTERISABLE KEY FORMAT FOR PLANT IDENTIFICATION

Many of the paper-based/printable taxonomic key formats available for plant identification are fraught with inadequacies: fixed sequence of steps, non- readily amenable to automation or computerisation, lack of provision for confirmation of suspected plant identity and indeterminable character states, tedious construction and navigation procedures, and inability of users to ascertain the extent of reliability of the identification process. Aimed at making the practice of plant taxonomy more attractive, less laborious and dreaded, this paper proposes the calculable-contents identification matrix (CCIM), a new key format with structural and functionality attributes to circumvent some of the enumerated challenges. The status and prospects of CCIM are discussed with reference to the inadequacies observed in the dichotomous key format with which most taxonomists are familiar. Based on its features, applicability and potential outputs, CCIM is adjudged to be a useful template for reliable manual and electronic methods of plant diagnosis.

Authors

Publication Details

Journal
International Journal of Artificial Intelligence & Applications
Published
2025-09-28
DOI
https://doi.org/10.5121/ijaia.2025.16505
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CALCULABLE-CONTENTS IDENTIFICATION MATRIX: A COMPUTERISABLE KEY FORMAT FOR PLANT IDENTIFICATION

Adepoju Tunde Joseph Ogunkunle
International Journal of Artificial Intelligence & Applications
Smart Agriculture and AI
article

CALCULABLE-CONTENTS IDENTIFICATION MATRIX: A COMPUTERISABLE KEY FORMAT FOR PLANT IDENTIFICATION

Adepoju Tunde Joseph Ogunkunle
article en

Abstract

Many of the paper-based/printable taxonomic key formats available for plant identification are fraught with inadequacies: fixed sequence of steps, non- readily amenable to automation or computerisation, lack of provision for confirmation of suspected plant identity and indeterminable character states, tedious construction and navigation procedures, and inability of users to ascertain the extent of reliability of the identification process. Aimed at making the practice of plant taxonomy more attractive, less laborious and dreaded, this paper proposes the calculable-contents identification matrix (CCIM), a new key format with structural and functionality attributes to circumvent some of the enumerated challenges. The status and prospects of CCIM are discussed with reference to the inadequacies observed in the dichotomous key format with which most taxonomists are familiar. Based on its features, applicability and potential outputs, CCIM is adjudged to be a useful template for reliable manual and electronic methods of plant diagnosis.

International Journal of Artificial Intelligence & ApplicationsVol. 16(5)
Openalex Percentile: Top 79%
Smart Agriculture and AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.