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Satellites in Agriculture: Using Artificial Intelligence to Improve Satellite Imagery

 





satellites are observing earth24 7 and historical imageries of the farm you manage are available from multiple satellite sources this historical data could give you some specific insights about what happened and how it happened in the farm historical data could also be fed into artificial intelligence models to predict what will happen let's illustrate this with a wheat and canola farm in Saskatchewan Canada knowing the coordinates of the farm we cured its historical data of 2015 to 2020 whenever cloud coverage of the scene was less than 30 percent we evaluated three indexes normalized difference vegetation index or ndvi green chlorophyll index or gci and normalize difference water index or ndwia low value on ndvi means lack or poor vegetation for gci it means plant stress and forndwiit means water deficiency in the farm we collected satellite data from may 15 2015just before summer started and continued until the end of the harvesting season, in2020and plotted the historical vegetation index of the farm each data point in the graph is the average of ndviof the farm for the date satellite image was taken next week and next week by the end of plants are emerging in some areas of the farm most of the farm is covered by plant canopy by early June in late June although most of the farm is doing well vegetation is very poor in the marked area with too much stress in plants water index shows this is not because of water deficiency, on the other hand, some spots of the farm show mild stress and delayed vegetation which directly is the result of lack of water two weeks later the situation is much-improved however around late July water deficiency significantly Affected plants and reduced vegetation this water deficiency was over by nextWeek and plants recovered fast from there it's the end of summer and harvesting is close by mid-September, the farm is entering the winter mood this season was just an example now let's fast forward next five years and see how things fold out this graph summarized the performance of this farm over six years however it was a simplified approach that takes the average over the whole farm the satellite data from the farm could be segmented into many subplots of the desired size and each subplot has its own performance history Performances of subplots are not the same some show consistent under or over Performance compared to others for example here the subplot highlighted in green consistently over performed the one highlighted in red here is another example each subplot could be as small as only one pixel of the satellite image which corresponds to 10 meters by 10meter area on farm-scale each historical curve from each pixel or subplot indicates its future performance although such a data is overwhelming for the human mind artificial intelligence models can digest and extract hidden patterns that knowing what happened on the farm is not just about learning from history but also predicting the future and taking a proactive approach we feed historical values from satellite imagery and use of artificial intelligence and time-series analysis to predict the performance of farms as a whole or subplots as small as a pixel size in a satellite image let's get in touch and discuss how a combination of satellite remote sensing and artificial intelligence could benefit the farm you manage

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