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Plant Science Today (PST)

Research Articles

Vol. 13 No. sp2 (2026): Recent Advances in Agriculture

Application of machine learning tools for eco-strategising Fusarium wilt management of tomato caused by Fusarium oxysporum f. sp. lycopersici

DOI
https://doi.org/10.14719/pst.10010
Submitted
12 June 2025
Published
29-05-2026

Abstract

Fusarium wilt of tomato, caused by Fusarium oxysporum f. sp. lycopersici (FOL), severely impacts global tomato production. A field experiment evaluated 25 treatments, categorised as compost-based [Farmyard manure (FYM) + de-oiled sweet basil compost], biological control (FYM + bio-agents) and chemical fungicides under field conditions. A random forest machine learning (ML) model predicted treatment effects, validated through biological assays and statistical tests. The treatment combining FYM + de-oiled sweet basil compost (25 t/ha) with Trichoderma viride (5 kg/ha) and Pseudomonas fluorescens (5 kg/ha) demonstrated the highest efficacy. It reduced disease incidence while maintaining soil health. The model revealed antagonism between bio-agents and fungicides. This study highlights ML's potential in optimising integrated disease management (IDM), reducing resource use and expediting decision-making. The validated model provides a reliable decision-support tool for precise and sustainable management of Fusarium wilt in tomato.

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