Search
Red-green-blue to normalized difference vegetation index translation: a robust and inexpensive approach for vegetation monitoring using machine vision and generative adversarial networks
Sources of information

Precision Agriculture | Mar 7, 2023

Researchers from the University of Prince Edward Island in Canada have developed an innovative and cost-effective protocol for monitoring plant health in agricultural fields using high-resolution multispectral imaging. By leveraging machine vision (MV) and generative adversarial networks (GAN), they were able to convert standard red-green-blue (RGB) imagery captured by unmanned aerial vehicles (UAVs) into valuable normalized difference vegetation index (NDVI) maps.

Traditionally, NDVI maps were generated from near-infrared (NIR) imagery, but this study directly translated RGB imagery into NDVI, making it more accessible and affordable. The researchers tested the protocol using a fixed-wing UAV equipped with a RedEdge-MX sensor to capture images from different potato fields throughout the 2021 growing season.

By training and evaluating GAN models, particularly Pix2Pix and Pix2PixHD, they found that Pix2PixHD outperformed Pix2Pix in terms of accuracy and performance. The protocol demonstrated breakthrough results, enabling cost-effective monitoring of vegetation and orchard health. The trained GANs can generate useful vegetation index maps for precision agriculture practices, including variable rate applications. Additionally, the protocol has the potential to analyze remote sensing imagery of large-scale agricultural fields and commercial orchards, extracting essential information about plant health indicators.

This study presents an exciting advancement in economically monitoring plant health and offers valuable insights for precision agriculture and remote sensing applications.

Generated dataset sample for the training of generative adversarial networks used in the design of the proposed protocol. The left image represents the input image which is the combination of the red, green, and blue channels, while the target image is the NDVI image which is the computation index of the red and near-infrared channels.

 

 

 

 

Viewed Articles
Red-green-blue to normalized difference vegetation index translation: a robust and inexpensive approach for vegetation monitoring using machine vision and generative adversarial networks
Precision Agriculture | Mar 7, 2023Researchers from the University of Prince Edward Island in Canada have developed an innovative and cost-effective protocol for monitoring plant health in agricultura
Read More
AnimalAccML: An open-source graphical user interface for automated behavior analytics of individual animals using triaxial accelerometers and machine learning
June 2023 | COMPUTERS AND ELECTRONICS IN AGRICULTUREThe University of Georgia conducted a study to design and develop a user-friendly tool for customized machine learning model development and animal
Digitalization, sustainability, and coffee. Opportunities and challenges for agricultural development
May 2023 | AGRICULTURAL SYSTEMS The University of Hohenheim in Germany, in collaboration with researchers from Austria, conducted a study to assess the potential of digital technologies in addressing
An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security
January 19, 2023 | Scientific Reports |  Introduction: Climate change threatens agriculture, infrastructure, and local communities. Monitoring and predicting climate impacts on food security is essent
Enhancing climate change resilience in agricultural crops
December 04, 2023 | Current Biology |  Introduction: To ensure food security for a burgeoning global population, a 28% increase in global agricultural production is required over the next decade. Howe
Enhancing Energy Efficiency of Greenhouses using AI-based Climate Control
February 28, 2023 | Advances in Applied Energy |  Introduction: Researchers from Cornell University in USA proposed the use of novel artificial intelligence (AI)-based control framework to enhance the
TOP