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The rice production estimation in Indonesia conducted by several government agencies, among others: Central Statistics Agency (BPS), Director General of Food Crop Production and Horticulture, Ministry of Agriculture, and the Logistics Affairs Agency (BULOG). The approach used to predict rice production from each of these institutions is different from one another. Because of the different approaches the information obtained varies, making it difficult for users of information to utilize existing data. The estimation are based on the plot sampling prior to the harvest. This technique are not only laborious but also time consuming and need large funding support. In order to overcome these limitations, we applied the oblique photographic analysis technique to cover large area to predict the potential yield. Oblique Photographic Image skews in the air. This photograph done by shooting the near-mature rice crops with unmanned aircraft carrying the carrying camera that has a slightly angled. This skewing aerial photography has the character of the aerial photograph that looks slightly tilted but covering larger area compared to the vertical shooting. The yield prediction conducted by image processing software applying Neural Network method of MLP (Multi-Layer Perceptron). The result shows the neural network classification technique on oblique image can determined the rice field productivity especially for low productivity plots with overall accuracy of 69.23 percent.