Intelligent Control System Of Water And Fertilizer In Greenhouse Based On Tomato Phenotype Discrimination And Growth Environment Prediction

In this paper, the real-time greenhouse tomato phenotypic parameters data were obtained, and the current tomato growth and growth status were identified based on the in-depth learning method. Combined with the tomato phenotypic parameters data under the optimal water and fertilizer conditions, the comparative analysis was carried out. According to the difference of phenotypic parameter data of tomato, the deficiency status of water and fertilizer was identified, and the target scheme of water and fertilizer was determined. Based on LSTM neural network model, the predicted value of environmental parameters was obtained, and compared with the standard value of environmental parameters for the purpose of stopping irrigation and fertilization.