Authors : Mansoon Mangrulkar; Mahesh Hattimare; Mayur Mahale; Om Supare; Minakshi Ramteke

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/23sbj4j9

Scribd : https://tinyurl.com/34njnk9k

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV810

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Wheat is a cornerstone of global food security, but its production faces significant challenges from fungal diseases that can drastically reduce yield and quality. Traditional methods for detecting these diseases, such as visual inspections, are labor-intensive and often prone to error due to subjectivity and variability in expertise. Recent advances in artificial intelligence (AI) and deep learning (DL) [1] provide potential alternatives for automated and highly accurate illness identification. This study focuses on applying Convolutional Neural Networks (CNNs) to identify common wheat diseases, leveraging the model's capability to learn multifaceted patterns directly from images. By employing techniques such as transfer learning, we fine- tune pre-trained CNN models on domain-specific datasets, enhancing accuracy even with limited labeled data. Additionally, we explore the combination of these models into user-friendly applications that can assist farmers in current disease diagnosis in the field. This approach aims to streamline the detection process, enabling faster and more effective disease management. Our findings demonstrate that AI-driven solutions can significantly aid agricultural practices, with the potential to boost yield quality and support sustainable wheat production.

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