Patent AU-2021100962-A4 (AU)
Title: Smart irrigation enriched with fertilizer mixing and water wastage reduction using deep learning techniques
Inventor: SATHYAMOORTHY MALATHY
IPC: G06F17/18
Filed: 2021-02-21
Granted: 2021-04-29
Abstract: SMART IRRIGATION ENRICHED WITH FERTILIZER MIXING AND 
WATER WASTAGE REDUCTION USING DEEP LEARNING 
TECHNIQUES 
Abstract 
The major problem faced by today's world is water scarcity. If the effective usage of water is 
minimal when compared with the total amount of water drawn, then water wastage is more.  
Even though the water scarcity persists, agriculture have to be sustained in order to satisfy the 
food requirements of human race. With the help of recent technologies, we can design a 
smart irrigation model which in turn maximizes the amount of effective usage of water in 
agriculture field.  
In this work, three varieties of sensors are used such as climatic sensors, soil sensors 
and soil NPK sensors. The climate sensors assess the environmental conditions and gathers 
the details like temperature, humidity and wind speed. These are the main factors which are 
responsible for rainfall. If there is a possibility for excess rain or excess wind, then the 
irrigation to the agriculture field will be delayed to certain time. This feature of our model is 
developed with deep learning technique, which in turn improves the efficiency of the 
machine by learning from the previous results. The soil and NPK sensors gathers the details 
like moisture, NPK nutrients traces in the soil of the agriculture field. The input fed to this 
system is, soil type, relative humidity and plant type. The output will be suggestion predicting 
the need for irrigation with a period and also the amount of fertilizer needed to provide the 
maximum yield in an agriculture field.  
The radio circuit model gathers the details from climate sensors and soil sensors. The 
data measurements of the parameters such as temperature, humidity and wind speed will be 
given as input to the input layer of the Convolutional Neural Network (CNN). The processing 
will take place in the hidden layer and the following outputs will be produced.  
• Due to the prediction of excess wind in the near future, the distribution of water in 
unnecessary area will be avoided.  
• The system could provide a suggestion regarding the irrigation at right time and right 
quantity.  
• The duration of irrigation for the agriculture field is predicted accurately with the help 
of moisture sensors.  
1
Deep learningmodule 
Excess rain moisture 
or wind in the enar No It content < No 
future threshold 
No need for 
irrigation and 
mixing of 
Yesfetlzr 
External Rain sensors 
Yes 
weather 
condition 
detection Delay the 
windsensors irrigation NPK traces <No windsensrs -+ prcessthreshold 
Radio circuit process 
model 
soil moisture 
sensor 
Yes 
Agriculture 
field 
Soil NPK Estimate and 
sensor mix the 
fertilizer 
needed 
Accurate mixing proportion offertilizer as per the NPK traces available in the soil surface 
Convolutional .  
Neural Estimating the right quantity of irrigation at the right time Suggestions 
Avoiding wsatage of water by predicting the near future environmental conditions 
Figure 1. Smart irrigation by incorporating data from climate, soil and NPK sensors and deep 
learning techniques 
2