Title: Deep learning and machine learning approaches for data-driven risk management and decision support in precision agriculture

Authors: Mounia Mikram; Chouaib Moujahdi; Maryem Rhanoui

Addresses: LYRICA Laboratory, School of Information Sciences, Rabat, Morocco ' Scientific Institute, Mohammed V University in Rabat, Rabat, Morocco ' LYRICA Laboratory, School of Information Sciences, Rabat, Morocco

Abstract: Modern agriculture grapples with challenges such as unpredictable weather, biosecurity threats, market volatility, evolving regulations, and farmer health concerns. Effectively addressing these issues while maintaining sustainability demands informed decision-making. Data-driven technologies, especially deep learning (DL), emerge as crucial solutions. This study introduces a sustainable multivariate risk management system for precision agriculture, encompassing plant disease detection, weed detection, fire and smoke detection, and crop recommendation modules. Empowering farmers with tools to navigate risks and enhance operational efficiency, the system leverages DL techniques to uncover correlations among diverse risk factors. Enabling well-informed decisions on risk mitigation, this innovative system has the potential to revolutionise precision agriculture, fostering sustainability and profitability. Insights from the study set a benchmark for adopting data-driven, sustainable practices in smart agriculture. Farmers can utilise the system to conduct informed assessments, proactively mitigate crop damage, and redefine their approach to modern agriculture, ensuring improved yields and enhanced monitoring.

Keywords: deep learning; precision agriculture; risk management; farming; risk mitigation strategies; smart agriculture.

DOI: 10.1504/IJSAMI.2025.145317

International Journal of Sustainable Agricultural Management and Informatics, 2025 Vol.11 No.2, pp.226 - 247

Received: 17 Nov 2023
Accepted: 29 Jan 2024
Published online: 31 Mar 2025 *

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