Analyzing and Improving the Agricultural Methods Using Machine Learning and IoT

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Pooja Sharma, Gautam Bhagat, Rishu Sharma


Approximately 70% of Indian population depends on agriculture directly or indirectly. Today India’s population is approaching 1.9 billion, and this is a huge number. As the population is increasing, demand of food is also increasing, however the old farming ways are not sufficient to meet this increasing demand. The only ways left are accuracy and increased production at fast rate, where increasing accuracy reduces waste of man power along with considering crop diseases and increased production meets food demand of increasing population. For these two ways, we have to automate farming practices and use machines instead of man power. For automation, we are going to use IoT and various fields like deep learning, cloud computing etc. In this paper, we have implemented and compared 5 different Machine Learning algorithms for accuracy and efficiency of our model.

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