  This research paper presents AMaizeD: An End to End Pipeline for Automatic
Maize Disease Detection, an automated framework for early detection of diseases
in maize crops using multispectral imagery obtained from drones. A custom
hand-collected dataset focusing specifically on maize crops was meticulously
gathered by expert researchers and agronomists. The dataset encompasses a
diverse range of maize varieties, cultivation practices, and environmental
conditions, capturing various stages of maize growth and disease progression.
By leveraging multispectral imagery, the framework benefits from improved
spectral resolution and increased sensitivity to subtle changes in plant
health. The proposed framework employs a combination of convolutional neural
networks (CNNs) as feature extractors and segmentation techniques to identify
both the maize plants and their associated diseases. Experimental results
demonstrate the effectiveness of the framework in detecting a range of maize
diseases, including powdery mildew, anthracnose, and leaf blight. The framework
achieves state-of-the-art performance on the custom hand-collected dataset and
contributes to the field of automated disease detection in agriculture,
offering a practical solution for early identification of diseases in maize
crops advanced machine learning techniques and deep learning architectures.
