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


  • Ahmad Anwar Zainuddin kulliyyah of Information and Communication Technology, International Islamic University, Malaysia https://orcid.org/0000-0001-6822-0075
  • Shaun Tatenda Njazi Faculty of Science, Technology, Engineering, and Mathematics, International University of Malaya-Wales
  • Asmarani Ahmad Puzi Kuliyyah of Information and Communication Technology, International Islamic University Malaysia
  • Nur Athirah Mohd Abu Bakar Kuliyyah of Information and Communication Technology, International Islamic University Malaysia
  • Aly Mennatallah Khaled Mohammad Ramada Kuliyyah of Information and Communication Technology, International Islamic University Malaysia
  • Hasbullah Hamizan Silverseeds Lab Network, Gombak Selangor
  • Rohilah Sahak Silverseeds Lab Network, Gombak Selangor
  • Aiman Najmi Mat Rosani Computer Science (Computer System) University Putra Malaysia, Seri Kembangan
  • Nasyitah Ghazalli Research and Technology, Thales, United Kingdom
  • Siti Husna Abdul Rahman Faculty of Computing & Informatics (FCI), Multimedia University, Cyberjaya, Malaysia
  • Saidatul Izyanie Kamarudin Kolej Pengajian Pengkomputeran,Informatik dan Media (KPPIM) Universiti Teknologi MARA Shah Alam, Selangor




Machine learning, Computer vision, CNN, Maize


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.


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How to Cite

A. A. . Zainuddin, “Evaluating the Effectiveness of Machine Learning and Computer Vision Techniques for the Early Detection of Maize Plant Disease”, Malaysian J. Sci. Adv. Tech., vol. 3, no. 3, pp. 166–178, Aug. 2023.