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In order to enhance the economic benefits of fruit and vegetable cash crop facilities for agriculture, precise stereotactic of compensatory pollination technology in effective pollination period, is the effectively improve the fruit setting rate and the economic benefits of crop. The effects of honeybee pollination on the flowering can provide theoretical support for crop yield and ecological security. However, manual spraying of compensatory pollination technology is high, the working efficiency is low, and the control strategies based on single multiple UAV faces great challenges, which are the effects of flight heights on the droplet deposition and distribution. In this paper, firstly, flow chart of multiple unmanned aerial vehicles in facility agriculture for precise stereotactic of compensatory pollination method is given. Then, the thermogenesis and precise detection of the flower tissues is studied. Last, simulation of 16 UAVs and the effectiveness of the theoretical results is given in Matlab environment. Time varying formation control, formation tracking control of UAVs in facility agriculture, which based on precise stereotactic of compensatory pollination technology, provides a feasible solution scheme for ecological unmanned farm.

In the view point of ecological holism, deep neural networks can be view as a machine cognitive agent with overall multi-level nested structure, the representation learning and problem-solving ability of machine cognitive agent with multi-modal perception function is also the results from the perception and judgment of ambient situation environment. In the paper, a machine cognitive method based on ecological holism was deconstructed. Then, two core questions to characterize learnability of AlphaGo were answered, one is how to explain the characteristics of the hierarchical gradient and update parameters between different layers of networks? Two is how to understand the effective ability to choose the drop sampling at the strongest level of game of Go. These core controlling structural and functional roles proposed in this study may be further improved to a new type of artificial intelligence law of machine cognitive system.