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Deep Learning Models For Inventory of Agriculture Crops and Yield Production Using Satellite Images

Cropland mapping and classification is one of the vital steps in agricultural for planning and management activities at large and small scales. Crop mapping plays an important role in various applications such as crop inventories, crop insurance, yield estimation and the enforcement balanced maintenance. The combination of machine learning and observations from more advanced satellite images can be used for agricultural land classifications in a more efficient manner and with lower costs and reduced time than traditional classification method. Therefore in the present work deep learning models are applied to identify the crop fields (crop mapping) and classification of crops for the yield estimation. In the paper it is also present brief introduction to deep learning approaches and research works which are applied in the agriculture domain for variety recognition, yield estimation, quality detection, crop mapping, and organized yield production.