MACHINE LEARNING-BASED DECISION SUPPORT FOR DETECTING MAIZE PLANT DISEASE

Abstract

Maize is an essential crop for us humans, it can be both used in the manufacturing industry and also for foods, etc. The goal of this project is to develop a machine learning-based decision support system for detecting maize plant disease. In order to achieve this, it was necessary to analyse and review machine learning techniques and algorithms best for detecting maize disease. Google colab was used to build the model to detect the disease. The model was built to reduce cost and time of detecting maize plant disease. The model employs a convolutional neural network to identify specific diseases in maize and was able to get a satisfactory result. The model was able to accurately detect the disease, this improves the rate at which maize crop loss can be reduced due to infections or diseases which have infected the plants