Fungal Infection Detection in Wheat Leaves Using Machine Learning

Mansoon Mangrulkar; Mahesh Hattimare; Mayur Mahale; Om Supare; Minakshi Ramteke1

1

Publication Date: 2024/11/29

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.

Keywords: No Keywords Available

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

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24NOV810.pdf

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