Background: Over the centuries many revolutions have happened, lot of innovations took place, several discoveries changed the way the world would have been, these may be credited to discovery of paper, compass, steel, steam engine, anti-biotics, lenses, transistors, electricity, or computer. Civilizations have evolved but, in every era, agriculture played a major role in fueling the prosperity of the nation and establishing its growth. Agriculture in India provides livelihood to almost about 60% of India’s population. Percentage contribution of agriculture and allied sectors in gross value added for India at present prices stood at 17.8% in 2020. Investment through consumer in India should grow in 2021, post losses due to COVID-19 contraction. Food sector in India is set for huge growth owing to its immense potential for diversity and value addition. Food processing sectors contributes approximately 32% of total market share, supposedly one of biggest sectors and stands fifth in terms of production, consumption, export and expected growth.
Methods: Farming as a sector is a vulnerable investment owing to dependency on diversified geographical locations, environmental conditions and pest attacks, it is of prime importance to devise technological assisted methods to monitor and provide early remedial actions for the damage and infections to the crop. Work proposed is an effort to increase the yield a farmer would otherwise have; system implements the recommender system assisted by sensor data and weather conditions prevailing to suggest ideal crops to be sown for the region. Model acquires data: moisture content, rainfall estimated, NPK content of soil, temperature conditions and Ph value of the soil in addition to climatic conditions of the region for recommending the suitable crop.
Result: Model proposed estimated the required parameters employing calibrated sensors from a maize farm and climatic conditions from government weather prediction platform, crop gave us a small window of six months from June to November to conduct the experiment, site selected was at Safedabad, Lucknow (U.P.) and classification was achieved through random forest algorithm trained on labeled dataset which achieved an accuracy of 96%, the testing data when fed to the model gave output with an accuracy of 93%.
