Evaluating Performance of Deep Neural Networks for Crop Classification and Complex Facility Recognition Automatic identification of different crops purely using satellite imagery is an active area of research which helps the government in accurately estimating the yields of different crops, thereby helping them prepare proper action plans to ensure that the supply of food matches the demand. With rapid rise in the world population, this is an essential factor. On the other hand, detecting different types of complex structures such as coal plants, gas plants, nuclear power plants and airports is another emerging area of research. Detecting nuclear power plants is essential from a national security perspective, for monitoring nuclear proliferation. With the availability of powerful computing resources, deep neural networks have increasingly become the de facto standard in several image classification challenges. In this paper, we evaluate the performance of several wellknown deep neural networks (Convolutional Neural Networks) in performing the aforementioned two tasks  crop classification and complex facility recognition. Based on our experiments, we will evaluate how stateoftheart networks will perform in performing tasks such as crop classification and complex facility recognition

Deep localization model for intrarow crop detection in paddy field Abstract   Automated and precise rice plant localization is crucial for the mechanization of rice production, which can facilitate targeted spraying, sitespecific fertilization, and mechanized weeding etc. Existing approaches adopted thus far have mainly focused on interrow weed detection or rice seedling row detection. Nevertheless, techniques for intrarow individual rice plant positioning continue to face major challenges induced by the specific paddy field environments or complex morphology of rice plant. This paper proposed a new deep localization network for intrarow rice detection at the single plant level in a paddy field. This method designed a twostage model. The module in stage 1 identified potential locations containing rice plants in the entire image. The module in stage 2 predicted the confidence of rice plant identification and refined the corresponding box bounds. The twostage processing modules shared a deep backbone network for learning fullimage convolutional features and are combined into a unified framework to facilitate an endtoend training. In addition, we constructed a rice plant detection dataset and proposed a taskoriented evaluation method for performance verification of the algorithm. Experiment results showed the proposed deep model achieved a high localization accuracy of 93.22% and a high testing speed of 15 fps, verifying the effectiveness and efficiency of the method. Using this method, we can develop techniques for finerlevel agriculture production, such as spraying and weed control, to achieve healthy and economical rice yields.