Evaluating the Effectiveness of Machine Learning and Computer Vision Techniques for the Early Detection of Maize Plant Disease

• Published in: Malaysian Journal of Science and Advanced Technology Vol. 3; no. 3; pp. 166 - 178
• Main Authors: Zainuddin, Ahmad Anwar, Shaun Tatenda Njazi, Asmarani Ahmad Puzi, Nur Athirah Mohd Abu Bakar, Aly Mennatallah Khaled Mohammad Ramada, Hasbullah Hamizan, Rohilah Sahak, Aiman Najmi Mat Rosani, Nasyitah Ghazalli, Siti Husna Abdul Rahman, Saidatul Izyanie Kamarudin
• Format: Journal Article
• Language: English
• Published: Penteract Technology 29.08.2023
• Subjects: CNN; Computer vision; Machine learning; Maize
• ISSN: 2785-8901; 2785-8901
• DOI: 10.56532/mjsat.v3i3.180

Monitoring plant growth is a crucial agricultural duty. In addition, the prevention of plant diseases is an essential component of the agricultural infrastructure. This technique must be automated to keep up with the rising food demand caused by increasing population expansion. This work evaluates this business, specifically the production of maize, which is a significant source of food worldwide. Ensure that Mazie's yields are not damaged is a crucial endeavour. Diseases affecting maize plants, such as Common Rust and Blight, are a significant production deterrent. To reduce waste and boost production and disease detection efficiencies, the automation of disease detection is a crucial strategy for the agricultural sector. The optimal solution is a self-diagnosing system that employs machine learning and computer vision to distinguish between damaged and healthy plants. The workflow for machine learning consists of data collection, data preprocessing, model selection, model training and testing, and evaluation.