Disease attacks on crops like maize pose a significant threat to the global food supply chain in Africa, particularly in West Africa. Maize is a staple food source and the economic backbone of the population and farmers in West Africa. In recent years, maize yields have declined due to diseases. Systematic solutions, such as visual inspection through laboratory experiments for disease diagnosis, have not led to a significant improvement in production or ensured sustainable food security in Africa. In response to this challenge, we introduce a lightweight deep-learning ensemble model for early disease detection in maize plants. The study focuses on developing and validating a model specifically designed to identify and classify diseases in maize plants. We use computer vision technology to capture intricate patterns in maize leaf images. The model is trained to recognise six classes, with five representing different diseases and one representing a healthy state. In this paper, we explore the amalgamation of Residual Network (ResNet9) and Efficient-Net-b4 (ENetb4) as a pre-training model built on convolutional neural networks (CNN) to improve accuracy and robustness in maize disease detection and prediction. The results of the study indicate significant opportunities for improvement in agricultural technology in West Africa. For instance, the ResNet9 model accurately identified diseased images of maize crops with a performance accuracy of 98.2%, while the (ENetb4) model achieved a performance accuracy of 94.3% in the same task.
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