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