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
Soil organic matter content detection system based on high-temperature excitation principle
November 30, 2023 | Computers and Electronics in Agriculture |  Introduction: Precision agriculture involves using advanced technology to optimize crop growth, and soil organic matter for crop growth.
Adapting crop production to climate change and air pollution at different scales
October 16, 2023 | Nature Food |  Introduction: Air pollution and climate change are interconnected challenges that impact field crop production and agroecosystem health. Adapting crop production to t
Automated Imaging System for Insect Pest Monitoring
August 31, 2023 | Computers and Electronics in Agriculture |  Introduction: Outdoor cultivation of mango faces challenges from insect pests and environmental factors. Integrated Pest Management (IPM)
Predicting site-specific economic optimal nitrogen rate using machine learning methods and on-farm precision experimentation
Precision Agriculture | April 20, 2023Researchers from the University of Nebraska conducted a study to improve nitrogen (N) fertilizer management in winter crops like wheat and barley. By applying the
Internet of Plants: Revolutionizing Agriculture with Sensor Networks
August 03, 2023 | Nature Reviews Methods Primers | In the study conducted by researchers from Delft University of Technology and Wageningen University & Research, the focus is on introducing the conce
TOP