Evaluating the MobileNet50 CNN Model for Deep Learning-Based Maize Visualisation and Classification

Plant diseases present a substantial threat to global food security, and the timely detection of such diseases remains a challenging and time-consuming task. The accurate determination of a plant's health status and the identification of specific infections typically require the expertise of professionals. The advent of Deep Learning has significantly transformed the field of computer vision, offering highly efficient techniques for image analysis and categorization. This study specifically focuses on utilizing the MobileNet50 Convolutional Neural Network (CNN) model to visually represent and categorize images of maize. The identification of maize, a widely cultivated crop, is intricate due to its diverse array of varieties and growth stages. This research aims to leverage the capabilities of the advanced MobileNet50 CNN architecture to enhance the precision and effectiveness of maize classification. The model achieved an impressive accuracy of 97%, demonstrating its robust performance in distinguishing various types and health states of maize plants. By employing MobileNet50, this study contributes to the advancement of computer vision applications in agriculture, facilitating the prompt and accurate identification of maize diseases. The utilization of a deep learning approach reduces the dependency on human expertise, making it more accessible and efficient for large-scale agricultural monitoring. Ultimately, the integration of MobileNet50 in maize classification holds promise for revolutionizing plant disease detection and contributing to global efforts in securing food resources.