Open AccessCrop Recommendation System using Machine Learning
Author(s) -
Dhruvi Gosai,
Chintal Raval,
Rikin J. Nayak,
Hardikkumar S. Jayswal,
Axat Patel
Publication year - 2021
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit2173129
Subject(s) - agricultural engineering , environmental science , fertilizer , agriculture , population , water content , crop , computer science , machine learning , agronomy , engineering , geography , demography , geotechnical engineering , archaeology , sociology , biology
A vast fraction of the population of India considers agriculture as its primary occupation. The production of crops plays an important role in our country. Bad quality crop production is often due to either excessive use of fertilizer or using not enough fertilizer. The proposed system of IoT and ML is enabled for soil testing using the sensors, is based on measuring and observing soil parameters. This system lowers the probability of soil degradation and helps maintain crop health. Different sensors such as soil temperature, soil moisture, pH, NPK, are used in this system for monitoring temperature, humidity, soil moisture, and soil pH along with NPK nutrients of the soil respectively. The data sensed by these sensors is stored on the microcontroller and analyzed using machine learning algorithms like random forest based on which suggestions for the growth of the suitable crop are made. This project also has a methodology that focuses on using a convolutional neural network as a primary way of identifying if the plant is at risk of a disease or not.