36th Annual Student Research Conference
Plant Disease Detection Using Convolutional Neural Network
Shibam Pokhrel
Dr. Ruthie Halma, Faculty Mentor
Plant diseases pose a major threat to the global agricultural industry, often operating in a silent and lethal manner that can escalate quickly beyond our control. Early detection and classification of plant diseases using machine learning and Artificial Intelligence (AI) is one of the best ways to prevent catastrophic scenarios from occurring. The use of AI in the agriculture industry has increased substantially recently, providing an efficient and effective solution for identifying and detecting plant diseases. In this paper, we propose a Convolutional Neural Network model for the detection of diseases in four different plants using the images of leaves from an apple, tomato, potato, and corn. This research paper provides a viable solution for plant disease detection using a dataset of about 8,000 images from four different plant species. It shows that using CNNs is an ideal path for AI to detect plant disease in real-world agricultural applications.
Keywords: Artificial Intelligence, Plant Disease, Computer Science, Neural Network, Machine Learning, Image Processing
Topic(s):Computer Science
Biology
Mathematics
Presentation Type: Oral Presentation
Session: 109-4
Location: MG 1098
Time: 9:15