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This research paper presents AMaizeD: An End to End Pipeline for AutomaticMaize Disease Detection, an automated framework for early detection of diseasesin maize crops using multispectral imagery obtained from drones. A customhand-collected dataset focusing specifically on maize crops was meticulouslygathered by expert researchers and agronomists. The dataset encompasses adiverse range of maize varieties, cultivation practices, and environmentalconditions, capturing various stages of maize growth and disease progression.By leveraging multispectral imagery, the framework benefits from improvedspectral resolution and increased sensitivity to subtle changes in planthealth. The proposed framework employs a combination of convolutional neuralnetworks (CNNs) as feature extractors and segmentation techniques to identifyboth the maize plants and their associated diseases. Experimental resultsdemonstrate the effectiveness of the framework in detecting a range of maizediseases, including powdery mildew, anthracnose, and leaf blight. The frameworkachieves state-of-the-art performance on the custom hand-collected dataset andcontributes to the field of automated disease detection in agriculture,offering a practical solution for early identification of diseases in maizecrops advanced machine learning techniques and deep learning architectures.

AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection