Abstract
More than two-thirds of freshwater consumed worldwide are used for irrigation, and the existing control system for irrigation and fertilization is not intelligent and efficient enough. It is necessary to improve the utilization efficiency of irrigation water and fertilizer, and the intelligent level of irrigation and fertilization system. We have built tomato fertilizer deficiency and fertilization decision model based on leaf images and deep learning, this paper develops an Raspberry Pi-embedded intelligent control system for irrigation and fertilization based on previous deep learning models. The system included sensors, image acquisition, control part, control panel and remote control, the type of main hardware and sensors were selected, and the circuit of the system were designed. The C language is used to compile the source code of the deep learning framework, and the Python language is used to compress the deep learning model. The result shows that the compression rate of the deep learning model can reach 36.7% and the decision-making time can be shortened by 36% under the condition of ensuring the model accuracy. The Raspberry Pi-embedded intelligent control system can help farmers improve the utilization efficiency of irrigation and fertilizer.
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