A Review on Smart Farming based on Soil and Environmental Factors with Deep Learning Techniques

India's global economy is critically dependent on agriculture, which also accounts for a sizable portion of GDP. This paper provides a review of the use of machine learning and deep learning techniques in agriculture and elaborates different systems developed for smart agriculture including crop recommendations, fertilizer suggestions, categorization procedures and diseases identification by images. Climate factors like temperature, rainfall, soil quality, and fertilizers are the key determinants of agricultural productivity. Decision making such as determining the diseases and applying solutions to them is an important step in agricultural activities. With advanced technologies evolved in this digital world we can build more sustainable systems to make these farming procedures automated. Deep learning techniques are giving very prominent results in solving problems of predictions and decision making. The application of machine learning algorithms that are used in solving different problems that are arising in farming process are discussed. This paper contributes to know the available techniques for the automated farming procedures.