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

TL;DR

AMaizeD introduces an automated deep learning pipeline utilizing multispectral drone imagery for early maize disease detection, achieving high accuracy and aiding in crop health management.

Contribution

The paper presents a novel end-to-end framework combining multispectral imagery and deep learning for maize disease detection, with a custom dataset and state-of-the-art results.

Findings

Effective detection of multiple maize diseases.

High accuracy on a diverse, custom dataset.

Potential for early disease intervention.

Abstract

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.