Level of Education of Students Involved
Graduate
Faculty Sponsor
Michael Glass
College
College of Business (COB)
Discipline(s)
Computer Science
Presentation Type
Poster Presentation
Symposium Date
Spring 4-24-2025
Abstract
Corn production is significantly impacted by pests and diseases, leading to substantial yield losses and economic damage. Traditional detection methods are time-consuming, labor-intensive, and rely on expert knowledge. Artificial Intelligence (AI), specifically machine learning and computer vision techniques, can provide an efficient solution for pest and disease detection in corn. This study explores the use of AI models for automated detection and classification of pests and diseases affecting corn plants.
The dataset used for this research is the Corn Leaf Disease Dataset, which includes a total of 4188 images for classifying four types of corn leaf conditions: Common Rust (1306 images), Gray Leaf Spot (574 images), Blight (1146 images), and Healthy (1162 images). This dataset, derived from the PlantVillage and PlantDoc datasets, is publicly available for research and provides valuable resources for developing AI models aimed at detecting and classifying plant diseases in corn. Using this dataset, we trained AI models, particularly Convolutional Neural Networks (CNNs), to detect and classify plant health issues. Various AI algorithms were tested for accuracy and efficiency in real-time disease identification.
By integrating AI into pest and disease detection, this research could contribute to the advancement of precision agriculture, reducing the need for chemical treatments, optimizing resource utilization, and enhancing sustainable farming practices.
Recommended Citation
Dommati, Suma, "Pest and disease detection in corn with AI" (2025). Symposium on Undergraduate Research and Creative Expression (SOURCE). 1481.
https://scholar.valpo.edu/cus/1481
Biographical Information about Author(s)
Dommati is a graduate student pursuing an MBA in Business Analytics at Valparaiso University. She has over six years of experience as an Agriculture Extension Officer with the Department of Agriculture, Telangana Government, India. Suma is also a certified drone pilot. With a strong background in agriculture, she is passionate about integrating technology into farming. Her research interests focus on AI-driven solutions for precision agriculture, sensor-based automation, the Internet of Things (IoT), drone technology in agriculture, and sustainable farming practices.