Digital t echnologies in agricult ure and rural areas stat us report

Int.J.Curr.Microbiol.App.Sci (2018) 7(12): 2122-2128 
2125 
Fig.2 
Plant Stress recognition using machine learning and intelligence 
Fig.3 
Robotics in digital farming 
A detailed 3D map of the field, its terrain, 
irrigation drainage and soil viability must be 
developed using the drone. This has to be 
carried out before the crop cycle begins. 
The soil N
2
levels management can also be 
done by solutions powered by drone. Drone 
powered aerial spraying of pods with seeds 
and plant nutrients into the soil supplies 
necessary supplements for plants, also the 
drones can be programmed to atomize liquids 
by regulating the distance from the ground 
surface depending on the terrain. 
Crop monitoring and crop health assessment 
prevails as one of the most important domains 
in agriculture to offer dronebased solutions in 
coactions with computer vision technology 
and AI. 
Drones with high resolution cameras gather 
precision field images which can flow 

Int.J.Curr.Microbiol.App.Sci (2018) 7(12): 2122-2128 
2126 
through convolution neural network to detect 
areas with weeds, individual crops requiring 
more water, plant stress level in various 
growth stages.
In case of infected plants, by scanning crops 
in both RGB (Red Green Blue) and infra red 
light, potential multispectral images can be 
generated using drone devices. Through this 
individual and specific cluster of plants 
infected in any region of the field can be 
spotted and supplied with remedies at once.
The multi spectral images taken from the 
drone cameras blend hyper spectral images 
with 3D scanning techniques to define the 
spatial information system employed for acres 
of farm land. This renders guidance 
throughout the lifecycle of the plant as a 
temporal component. 

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