LeafCure 360: A Smart System for Medicinal Plant Detection, Remedies, and Cultivation Guidance

This paper presents LeafCure 360, an intelligent plant recognition system developed to identify medicinal plants and recommend herbal remedies using leaf images and artificial intelligence. The Pl@ntNet API is utilized for species detection from useruploaded images, while symptom-based matching logic suggests suitable plants for common ailments. Geolocation, soil data, and water access inputs enhance cultivation guidance through personalized recommendations. The system integrates ReactJS for the front end and Node.js for the back end to deliver seamless performance. Results indicate high identification accuracy and practical usability across multiple user groups. This study highlights the potential of combining traditional botanical knowledge with modern web technologies for accessible healthcare and farming support.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-24
DOI
https://doi.org/10.5281/zenodo.15726877
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

LeafCure 360: A Smart System for Medicinal Plant Detection, Remedies, and Cultivation Guidance

Zenodo (CERN European Organization for Nuclear Research)
Smart Agriculture and AI
article

LeafCure 360: A Smart System for Medicinal Plant Detection, Remedies, and Cultivation Guidance

article en

Abstract

This paper presents LeafCure 360, an intelligent plant recognition system developed to identify medicinal plants and recommend herbal remedies using leaf images and artificial intelligence. The Pl@ntNet API is utilized for species detection from useruploaded images, while symptom-based matching logic suggests suitable plants for common ailments. Geolocation, soil data, and water access inputs enhance cultivation guidance through personalized recommendations. The system integrates ReactJS for the front end and Node.js for the back end to deliver seamless performance. Results indicate high identification accuracy and practical usability across multiple user groups. This study highlights the potential of combining traditional botanical knowledge with modern web technologies for accessible healthcare and farming support.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 44%
Smart Agriculture and AI
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