The agricultural sector faces increasing challenges in ensuring sustainable and
efficient food production amid population growth, climate change, and evolving disease
threats. Among the most pressing issues in modern farming is plant disease
management, which directly impacts crop yield, food security, and economic stability.
Traditional disease detection and management approaches, such as visual inspection
and broad-spectrum chemical applications, often fail to provide timely, cost-effective,
and environmentally sustainable solutions. In response, the integration of smart
agricultural technologies has emerged as a transformative force in plant disease
management, steering global agriculture towards the paradigm of Agriculture 5.0.
This study explores the role of cutting-edge technologies, including the Internet of
Things (IoT), remote sensing, and AI machine learning (ML) and deep learning (DL), in
early plant disease detection and monitoring. By leveraging advanced sensor networks,
data analytics, and artificial intelligence, smart agriculture enables real-time disease
monitoring, improving diagnostic accuracy, reducing reliance on chemical treatments.
The research critically examines the strengths and limitations of these technologies and
assesses the transition from Agriculture 4.0 to Agriculture 5.0, highlighting the need for
a human-centric, sustainable, and technology-enhanced agricultural model.
Through an extensive review of current methodologies and case studies, this
dissertation presents a roadmap for the future of plant disease management processes
identifying key technological enablers, challenges, and adoption barriers. Findings
indicate that while AI-driven plant disease detection systems can offer unprecedented
accuracy and efficiency, widespread adoption is hindered by infrastructural deficiencies,
standardization issues, and socioeconomic factors. Bridging these gaps will require a
holistic approach that integrates technological advancements with policy support,
farmer education, and interdisciplinary collaboration.
(EN)
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