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IoT Based Crop Monitoring by using Machine Learning Algorithm

EasyChair Preprint no. 2428

4 pagesDate: January 20, 2020


Agriculture is the basic source of food supply in all the countries of the world—whether underdeveloped, developing or developed. Besides providing food, this sector has contributions to almost every other sector of a country. According to the report, 2017, about 17 % of the country’s Gross Domestic Product (GDP) is a contribution of the agricultural sector, and it employs more than 45% of the total labor force. In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture now-a-days is selecting the right crop for the right piece of land at the right time. Therefore, in our research we have proposed a method which would help suggest the most suitable crop(s) for a specific land based on the analysis of the data on certain affecting parameters like temperature, humidity, air quality and PH of soil using machine learning. In this paper we used geometric progression for predicting best suited crop in field.

Keyphrases: CO, GA, Humidity, ML, moisture sensor, Temperature

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Payal Nikam and Sunil Rathod},
  title = {IoT Based Crop Monitoring by using Machine Learning Algorithm},
  howpublished = {EasyChair Preprint no. 2428},

  year = {EasyChair, 2020}}
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