Agricultural Monitoring Trials
We’ve been refining our agricultural monitoring service which we will offer to farmers in Scotland and Northern England in the next few weeks. We have combined the MicaSense Sequoia Multispectral camera with a modified UAV for data collection. Using the Pix4D Mapper Pro software we can now produce high quality field data to supplement modern precision agriculture.
Currently there are several NDVI survey cameras on the market but many of these lack features and require difficult post processing to calibrate and geotag the resulting images. The Sequoia camera provides an all in one solution and integrates seamlessly with Pix4D Mapper Pro.
This light weight camera made it possible for us to modify a DJI Phantom 3 drone to carry it. Over larger UAV platforms the smaller Phantom class is light and efficient to fly as well as easily transportable when walking a long way over rough ground. We’ve flown hundreds of acres so far in our trials and it’s proven capable of capturing the imagery required for agricultural mapping.
Once we have acquired imagery data from the field it is processed using Pix4D Mapper Pro a professional mapping software that calibrates, combines and indexes thousands of images to create an indexed field map. By combining different light spectral bands red edge (RE) and near infra red (NIR).
The principle of NDVI analysis is that a healthy plant reflects more NIR than an unhealthy plant. Plants may look green but the RE and NIR bands of interest are unseen to the human eye. These along with RGB colour values may be added to different indices formulas to provide a plant health indicator.
The indexed map colour then gives a plant ‘health’ indicator from green for healthy plants, through to red for stressed plants. This data can be used to create an ‘agricultural prescription’ which is simply an application map file readable by modern farm equipment so that they can spray the correct amount of fertiliser or pesticide in the marked areas.
This health report can then be used along with farm based data (what, when has happened already) as well as point inspections to diagnose the problem (pest, nutrition, irrigation).
Further examples follow: