Machine Learning-Driven Soil Fungi Identification Using Automated Imaging Techniques

Applied Sciences 2026 Open access

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Authors: Karol Struniawski, Ryszard Kozera, Aleksandra Konopka, Lidia Sas‐Paszt, Agnieszka Marasek-Ciolakowska
DOI: 10.3390/app16020855
Cited by: 2
References: 53

Abstract

Soilborne fungi (Fusarium, Trichoderma, Verticillium, Purpureocillium) critically impact agricultural productivity, disease dynamics, and soil health, requiring rapid identification for precision agriculture. Current diagnostics require labor-intensive microscopy or expensive molecular assays (up to 10 days), while existing ML studies suffer from small datasets (

Fields of Study & Subjects

Cell Image Analysis Techniques Smart Agriculture and AI AI in cancer detection

Publication details

Journal / source
Applied Sciences
Publisher
MDPI AG
Volume
16
Issue
2
Pages
855–855
Publication date
14 January 2026
Language
EN