A mobile identification system for invasive plants based on adaptive hybrid feature correction-guard network model, supporting fully offline decision-making

To address the challenges of low efficiency and high cost in monitoring invasive plants in ecologically sensitive areas such as nature reserves, wetlands, and grasslands, this study has designed and developed an intelligent mobile offline recognition solution integrating the Adaptive Hybrid Feature Correction-Guard Network (AHFC-Guard Network). Through algorithm innovation and engineering optimization, this solution has built a mobile terminal recognition system suitable for complex environments, capable of identifying 38 major invasive plant species with an invasion level rated as grade I within China, providing a portable and efficient solution for ecological security protection. At the algorithm level, a novel Adaptive Hybrid Feature Correction Module (AHFCM) integrating the Hybrid Feature Correction Module (HFCM) and the Adaptive Channel Statistical Path (ACSP) is proposed. By cascading three AHFCMs to form the AHFC-Guard Network, it effectively suppresses complex background noise and enhances the stable discrimination between species. The experimental results show that the model has a parameter size of only 0.941 M and achieves an average recognition accuracy of 97.75% for 38 types of invasive plants. At the application function level, an offline mobile application for fieldwork has been developed. The installation package size is only 67.6 MB and it supports offline display of the Top-3 species recognition results. It embeds a database of 38 types of invasive plants' hazard characteristics, supporting graphic and text comparison queries for morphological features, ecological impacts, and control plans. This research provides full-chain technical support from species identification, hazard assessment to control decision-making for ecological protection departments. Through a high-precision and low-cost mobile monitoring solution, it significantly enhances the biosecurity protection efficiency in ecologically sensitive areas.

Authors

Institutions

Publication Details

Journal
Journal of King Saud University - Computer and Information Sciences
Published
2025-12-05
DOI
https://doi.org/10.1007/s44443-025-00396-6
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

A mobile identification system for invasive plants based on adaptive hybrid feature correction-guard network model, supporting fully offline decision-making

Wenli An, Yan Ren, Guoxin Li, Zhi Li et al.
Journal of King Saud University - Computer and Information Sciences
Smart Agriculture and AI
article

A mobile identification system for invasive plants based on adaptive hybrid feature correction-guard network model, supporting fully offline decision-making

Wenli An, Yan Ren, Guoxin Li, Zhi Li, Jiarong Yang, Haiming Ni
article en

Abstract

To address the challenges of low efficiency and high cost in monitoring invasive plants in ecologically sensitive areas such as nature reserves, wetlands, and grasslands, this study has designed and developed an intelligent mobile offline recognition solution integrating the Adaptive Hybrid Feature Correction-Guard Network (AHFC-Guard Network). Through algorithm innovation and engineering optimization, this solution has built a mobile terminal recognition system suitable for complex environments, capable of identifying 38 major invasive plant species with an invasion level rated as grade I within China, providing a portable and efficient solution for ecological security protection. At the algorithm level, a novel Adaptive Hybrid Feature Correction Module (AHFCM) integrating the Hybrid Feature Correction Module (HFCM) and the Adaptive Channel Statistical Path (ACSP) is proposed. By cascading three AHFCMs to form the AHFC-Guard Network, it effectively suppresses complex background noise and enhances the stable discrimination between species. The experimental results show that the model has a parameter size of only 0.941 M and achieves an average recognition accuracy of 97.75% for 38 types of invasive plants. At the application function level, an offline mobile application for fieldwork has been developed. The installation package size is only 67.6 MB and it supports offline display of the Top-3 species recognition results. It embeds a database of 38 types of invasive plants' hazard characteristics, supporting graphic and text comparison queries for morphological features, ecological impacts, and control plans. This research provides full-chain technical support from species identification, hazard assessment to control decision-making for ecological protection departments. Through a high-precision and low-cost mobile monitoring solution, it significantly enhances the biosecurity protection efficiency in ecologically sensitive areas.

Journal of King Saud University - Computer and Information SciencesVol. 38(1)
Northeast Forestry University (CN)
Openalex Percentile: Top 72%
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.