Alternative approaches of Machine Learning for Agriculture Advisory System

Machine learning is one of the recent trends. Currently it is being used in variety of interdisciplinary domains. The major contribution of GDP (Gross Domestic Product) of India belongs to agriculture production directly or indirectly. Most of the population in India is still dependent on farming or livestock for their regular income. Due to sufficient availability of solar energy, gifted by nature, in India we found the variety of crops. Major farmers hold fragmented land and adapt rain-feed cropping with traditional and repeated crop pattern. For increasing yield farmers add the fertilizers in extra quantity, which leads to soil degradation. Rather than repeated crop farmer should go for suitable crops, according available environmental condition. Here we have discussed machine learning approaches to develop Agriculture Advisory System. Comparative analysis of different supervised techniques with hybrid approach is done with the help of their performances.