Precise farming using IoT and Machine Learning

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  This is the sample circuit stimulation of available tools on the software

Inspiration

The growth of agricultural nations is greatly influenced by agriculture. Around 70% of Indians depend on agriculture, accounting for one-third of the country's GDP. The progress of the nation has often been hampered by agricultural issues. Smart agriculture, which modernizes the current conventional farming practices, is the only approach that can solve this issue. Smart agriculture greatly benefits from the Internet of Things (IoT).

What it does

We came up with the IoT based solution which includes soil analysis and also provides the suitable crops for the agricultural field based on soil. The analysis includes the temperature, moisture content, pH value, and NPK value(Nitrogen, Phosphorus and Potassium). Our solution also includes a machine learning model which guides the farmers increase/decrease the various parameters of the soil and make them suitable for the required crops. Our solution supports government schemes and provides connectivity to the grassroots audience.

How we built it

We are trying to build an IoT-based model using Arduino, OLED screens, various IoT sensors, and other electronic materials as a hardware and machine learning model based on our dataset for software purposes. We are using Django for deployment purposes and also using a local server for connectivity and data transmission.

Challenges we ran into

The major challenge we face is the dataset for the machine learning model at the same time working on the accuracy of the ML model. It was the challenge to create an authentic dataset of our own. The linkage part of the Internet of Things and Machine learning was also a little bit challenging as we aimed to make it hardware as well as software.

Accomplishments that we're proud of

We are planning to implement our model in the real world we have passed the ideation phase of the idea in college and also have an acceptance of the research paper at an IEEE conference for the same idea and model. We are applying for the patent of the idea.

What we learned

We learned the various soil components required for crop production and the various external parameters affecting the productivity of the farming fields. We have created a huge dataset of our own for the analysis of the soil. We are working on IoT and Machine learning together So, we learned the linkage of two technologies i.e. hardware as well as software.

What's next for Precise farming using IoT and Machine Learning

We are planning to make it accurate and workable for the fertilizers i.e. farmers will easily know about the missing component required for the soil to grow the particular crop. It will be immensely beneficial for farmers to grow the crops of their choice and need by using the missing component of the soil using our model.

Built With

• api
• arduino
• django
• iot
• machine-learning
• python