ICETETI-2024 | View PDF
Paper Id:ICETETI-MSM112
doi: 10.71141/30485037/ICETETI-MSM112

AI-Powered Disease Detection in Agriculture: A Deep Learning Approach To Plant Pathology

Olivia Brown, Karthikeyan Muthusamy

Citation:
Olivia Brown, Karthikeyan Muthusamy, "AI-Powered Disease Detection in Agriculture: A Deep Learning Approach To Plant Pathology" International Journal of Multidisciplinary on Science and Management, pp. 105-113, 2024.

Abstract:
Agriculture is a critical sector that ensures food security and economic stability worldwide. However, plant diseases pose significant threats to crop yield and quality. Traditional disease detection methods are often labor-intensive, time-consuming, and prone to errors. Recent advancements in artificial intelligence (AI), particularly deep learning, have revolutionized plant pathology by enabling automated, accurate, and early disease detection. This paper explores AI-powered disease detection, emphasizing convolutional neural networks (CNNs) and other deep learning models applied to plant pathology. We discuss various datasets, preprocessing techniques, model architectures, evaluation metrics, and real-world applications. The study highlights the challenges, including dataset limitations, computational costs, and model interpretability. Additionally, we propose a hybrid model integrating AI with Internet of Things (IoT) sensors to enhance real-time monitoring. The results indicate a high accuracy rate in disease classification, supporting the feasibility of AI-driven plant disease diagnosis. Future research directions focus on improving model generalization, expanding datasets, and integrating multi-modal inputs for enhanced decisionmaking. This research aims to contribute to precision agriculture, ultimately increasing crop yield and sustainability.

Keywords: Plant Pathology, Deep Learning, Convolutional Neural Networks, Precision Agriculture, AIPowered Disease Detection, Smart Farming, Crop Health Monitoring.

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