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today Im going to present extended agriculture Vision an extension of a large area image data set for agricultural pattern analysis nowadays acquiring a large amount of payable data in remote sensing and agriculture for machine learning tasks is extremely difficult this is due to the fact that labeling those kind of data sets often requires very strong and relative domain knowledge while researchers do devoting a large amount of effort to create new supervised and unsupervised data another branch of focus is about application as a self-supervised learning measure in this paper we are trying to address this issue from both of those two practice first we release extended agricultural Vision data based on that we Benchmark contrast with learning approaches are Downstream classification and semantic certification tests extend their agriculturation is composed of the two parts first a fulfilled version of a supervised agriculturation data set second unable raw images for unsupervised prin