Patent AU-2021105327-A4 (AU)
Title: A computer implemented and IoT based method for increasing crop production using machine learning model
Inventor: SINGH DHARMENDRA
IPC: G16Y10/05
Filed: 2021-08-11
Granted: 2021-11-11
Abstract: A computer implemented and loT based method for increasing crop 
production using machine learning model 
The present invention relates to the field of Internet of Things (loT) and agricultural crops.  
More specifically, the present invention relates to the field of increasing crop production 
using loT sensors and machine learning model. In today's world, with the advancement in 
technology, increasing farming yield with the use of the current technology will boost the 
growth of a nation as well as individual farmers. The present system is a combination of 
various loT sensors, central server, database, client devices and expert devices. The 
agricultural field contains various kind of sensors e.g. soil moisture sensors, environmental 
humidity sensors, rain sensor, temperature sensor, air-flow sensor, a camera unit and soil 
nutrients determination sensor. These sensors are operated by solar powered battery.  
These sensors use ZigBee chip integrated wireless sensors and data collecting modules and 
communicates to server through wireless communicating modules. The sever collects data 
at predetermined intervals and analyses the said data. The server machine utilizes the data 
from the database and database is equipped with initial data about agricultural aspects.  
Further, the server machine is trained on the data from the database through deep learning 
algorithm based on machine learning model (MLM) for making an artificial intelligent model 
to suggest the individual farmers regarding growth of the current agricultural crop. The 
captured/collected data from various loT sensor modules is analysed by the analyser 
module and alarm the individual farmers regarding their individual agricultural fields based 
on the results of the analyser and individual farmers act accordingly. The individual farmers 
are already linked with their corresponding agricultural field based on the registration done 
on the central server machine. Further, the said system can remotely, by the mobile 
application, start the irrigation system installed in the agricultural field using the solenoid 
valve as there is a need to irrigate the field and determined by the machine learning model.  
The proposed system also provides the feature of providing expert advice in need. If the 
server alarms any abnormal situation to an individual farmer about crop and suggest for an 
expert advice. The server then sends the query or abnormal situation to the farming expert 
if requested by an individual along with the images of the agricultural field taken by camera 
sensor to aid in providing suitable advice. The suggested advice by the farming expert is 
then forwarded to the client device by the server to act accordingly. The alarms and advice 
may be provided to the client device as per the language selected by the individual for 
communication and the server machine is equipped enough to translate and provide the 
alarms and advice to the individual farmer in their selected language. Thus, the present 
system automatically increases crop production using current technology and farmers will 
get benefited of the technology.  
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Training the server with the machine learning model using 
large database and test cases of agricultural aspects (101) 
captures data from various loT sensors at predetermined 
interval and transmit to central server through 
communication module (102) 
Server analyses the collected data with the database stored 
using adopted machine learning model (MLM) (103) 
Generating and sending an alarm to client application of a 
specified user along with request for expert advice if 
needed(104) 
starting remotely, by the client application, 
irrigation system installed in field using solenoid 
valve as determined by the machine learning 
model and notifies to the farmer (105) 
Figure 1 - Flow-diagram of the method for increasing crop production using loT sensors and 
machine learning model in accordance with present invention 
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