Deep Learning Technique for Crop Classification to Improve Accuracy

Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The complete factor of the crop yield depends on timely monitoring and suggestion .Crop classification based on high resolution satellite images are essential for supporting sustainable land management. The most challenging problems for their producing are collecting of ground based training and validation datasets. To increase the efficiency of ground data utilization it is important to develop classifiers able to be trained on the data collected in the previous seasons. In this study, we propose new deep learning method for providing crop classification maps satellite images. Main idea of the study is to utilize deep learning approach based on segmentation. Taking into account that collecting ground truth data is very time consuming and challenging task, the proposed approach allows us to avoid necessity for annual collecting of data and reducing the human efforts and resources. The research study clearly gives the idea and need of deep learning in the field of agriculture. The results were analyzed and the future perspectives were drawn with the obtained outcome.