Evaluating the effectiveness of machine learning and computer vision techniques for the early detection of maize plant disease

Njazi, Shaun Tatenda and Zainuddin, Ahmad Anwar and Ahmad Puzi, Asmarani and Mohd Abu Bakar, Nur Athirah and Ramada, Aly Mennatallah Khaled Mohammad and Hamizan, Hasbullah and Sahak, Rohilah and Mat Rosani, Aiman Najmi and Ghazalli, Nasyitah and Abdul Rahman, Siti Husna and Kamarudin, Saidatul Izyanie (2023) Evaluating the effectiveness of machine learning and computer vision techniques for the early detection of maize plant disease. Malaysian Journal of Science and Advanced Technology, 3 (3). pp. 166-178. E-ISSN 2785-8901

PDF - Published Version Restricted to Registered users only Download (784kB) | Request a copy

Abstract

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 tokeep up with the rising food demand caused by increasingpopulation 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. Toreduce 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.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Machine LearningComputer VisionCNNMaize
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T173.2 Technological change
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Ts.Dr. Ahmad Anwar Zainuddin
Date Deposited:	08 Sep 2023 16:52
Last Modified:	16 Jan 2024 17:26
URI:	http://irep.iium.edu.my/id/eprint/106493

Actions (login required)

View Item