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Satellites for Agriculture: Application of Artificial Intelligence for Satellite Imagery in Farming
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- Опубликовано: 6 июл 2021
- Application of remote sensing and satellites for agriculture are expanding fast during past few years. The major advantage of satellite data compared to other precision agriculture methods is universal availability of such data - whether or not the farmers take advantage of that. In this video, we illustrated the changes of Normalized Difference Vegetation Index (NDVI) in a wheat and canola farm in Saskatchewan, Canada over 6 years. The performance of the farm was investigated as an average and was compared to segmented subplots.
Although extracting insight from each subplot of the farm is overwhelming for the human mind, artificial intelligence models can digest and extract hidden patterns out of that. Grain Data Solutions feed historical values from satellite imagery and use artificial intelligence and time series analysis to predict performance of farms as whole, or on the subplots as small as pixel size in satellite image.
(music: bensound)
Brilliantly Explain!
thanks for sharing! great research.
important piece
for such small scale i.e. at farm level did you use sentinel-2 data? How good was predicted value with actual value of crop yield while using these data ?
S2, L8 and L7, all could work well on scale bigger than a few acres. Commercial satellites are good, but we know many farmers around the world cannot afford it.
This is very cool.
nice video and explanation !, where did you get the data ?, could you share some URL?
thanks for the video
We use a combination of satellite data including Sentinel and Landsat 7 and 8.
i want train a model like this please guide me
Are you certain about your interpretation of NDWI?
John, note there are two NDWIs, one detects water bodies, and one detect water in vegetation. We used the latter one. It's based on the paper: "NDWI A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space", by B. Gao. Let us know your thoughts on this.
@@graindatasolutions the spatial structure of all 3 index maps appears to be the same even though the colors used are different. The Gao paper you cited indicates that NDWI values increase with leaf area, and so do both of the other indices you used. NIR/Green and NDWI are less prone to saturate than NDVI but it's clear all 3 are mapping variation in leaf area. Drought and water stress are not the only factors that can cause a decrease of values for NDWI or any other plant vigor index. Therefore NDWI (or NDVI, etc) must be used with other data about rainfall or soil moisture (texture, compaction, etc) in order to *attribute* variation in index values to water deficiency. I'm not sure that NDWI represents "water content on a farm", so I asked for clarification on why you interpreted NDWI the way you did.
which website or application you are using for the crop monitoring?
We are a data science/AI company and use our own proprietary codes.
hello.Can you kindly send me a code of how i canexport to drive the NDVI map of a single field
what platform are you working on? One way would be using google earth engine JS platform, if this is for non-biz activity.
I am a student doing a project on wheat monitoring and yield prediction using optical satelite imagery.I use GEE@@graindatasolutions
How can I make such an animation? .. With thanks
How can I make such an interactive presentation? .. With thanks
If you can code in python, use GEE, or JS on Google Earth Engine are easy to learn
Sir pls can u share the code with me pls sir its really urgent
you got the code? what did you do for your project?