# Development of a Multispectral Image Database in Visible–Near–Infrared for Demosaicking and Machine Learning Applications

## TL;DR

This paper introduces a freely accessible multispectral image database for plant and weed research, supporting demosaicking and machine learning applications.

## Contribution

The novel contribution is the creation of a high-resolution multispectral image database with annotated data for research in demosaicking and deep learning.

## Key findings

- The database includes raw and demosaicked multispectral images of plants and weeds.
- It supports research in demosaicking techniques and segmentation algorithms.
- The database is intended for use in deep learning applications for crop/weed discrimination.

## Abstract

The use of Multispectral (MS) imaging is growing fast across many research fields. However, one of the obstacles researchers face is the limited availability of multispectral image databases. This arises from two factors: multispectral cameras are a relatively recent technology, and they are not widely available. Hence, the development of an image database is crucial for research on multispectral images. This study takes advantage of two high-end MS cameras in visible and near-infrared based on filter array technology developed in the PImRob platform, the University of Burgundy, to provide a freely accessible database. The database includes high-resolution MS images taken from different plants and weeds, along with annotated images and masks. The original raw images and the demosaicked images have been provided. The database has been developed for research on demosaicking techniques, segmentation algorithms, or deep learning for crop/weed discrimination.