Maize, a globally significant crop, is crucial to global food security and economic stability. Its 1.1 billion metric tons in 2020 production volume makes it the most cultivated cereal grain worldwide [1]. It serves as a staple food for over a billion people and is integral to biofuel production, animal feed, and various industrial applications [2], [3].

However, maize is susceptible to many diseases, including Cercospora leaf Spot, Common rust, and Northern leaf blight, which can significantly reduce its yield and quality, causing substantial economic losses globally [4], [5].

Among the most destructive maize diseases are Fusarium ear rot, caused by Fusarium verticillioides, which can reduce maize yields by up to 70% while also contaminating crops with harmful mycotoxins, and Maize Lethal Necrosis (MLN), a disease caused by the co-infection of the maize chlorotic mottle virus and sugarcane mosaic virus [6], [7]. Since its first detection in 2011, MLN has caused widespread damage, particularly in East Africa, spreading to countries like Kenya, Uganda, and Tanzania [8], [9]. These diseases emphasize the urgent need for early and accurate disease diagnosis to manage their impact on crop production and ensure food security [10], [11].

Traditional disease detection methods, such as manual inspection, are labor-intensive, time-consuming, and prone to errors, often resulting in delayed interventions [12]. In contrast, advances in deep learning, particularly Convolutional Neural Networks (CNNs), have transformed disease detection by enabling automated, precise analysis of leaf images [13]. These models can incorporate various image formats like RGB, grayscale, and segmented images, providing complementary insights that improve accuracy and robustness [14].

This study explores an integrated approach combining image processing and machine learning to detect maize diseases effectively. By employing advanced preprocessing techniques, feature extraction, and deep learning models, our methodology aims to achieve high accuracy in disease classification. This research aims to develop a robust, scalable solution for early disease detection, empowering farmers to manage maize diseases more effectively and enhance crop productivity.

This study converts a single maize leaf image into three formats: 1) RGB, 2) grayscale, and 3) segmented. Data augmentation techniques like rotation and flipping were applied, and all images were resized to 244×244 pixels. Each image format is processed by a separate CNN, and the final disease class prediction is determined using a majority voting mechanism. The model achieved an accuracy of 94.3%, demonstrating the effectiveness of the multi-format approach for maize disease detection.

This study uses deep learning to present a multi-format image analysis approach for maize disease detection. By combining RGB, grayscale, and segmented images, the model achieved an accuracy of 94.3%. CNNs and ensemble voting significantly improved the model's reliability, providing an effective and scalable solution for early disease detection. This can help reduce crop losses and enhance agricultural productivity.

Future work can focus on refining the model's accuracy by exploring advanced deep learning architectures and improving image preprocessing. The model can also be expanded to detect a wider range of diseases and applied to other crops. Integrating real-time monitoring systems and developing mobile apps could make the technology more accessible to farmers, promoting timely interventions and supporting sustainable agriculture.

No specific funding was received for this research.

The data supporting the findings of this study are available upon reasonable request from Kuna Hasada.

The authors declare no conflicts of interest in this work.