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Cross-modal hashing methods have received wide attention in cross-modal retrieval owing to their advantages in computational efficiency and storage cost. However, most existing deep cross-modal hashing methods cannot employ both intra-modal and inter-modal similarities to guide the learning of hash codes and ignore the quantization loss of hash codes, simultaneously. To solve the above problems, we propose a fine-grained similarity semantic preserving deep hashing (FSSPDH) for cross-modal retrieval. Firstly, this proposed method learns different hash codes for different modalities to preserve the intrinsic property of each modality. Secondly, the fine-grained similarity matrix is constructed by using labels and data features, which not only maintains the similarity between and within modalities. In addition, quantization loss is used to learn hash codes and thus effectively reduce information loss caused during the quantization procedure. A large number of experiments on three public datasets demonstrate the advantage of the proposed FSSPDH method.

An experiment was conducted to create a science-based program of irrigation and fertilizer application for two-year winter wheat under wide ridge and furrow irrigation in the Yellow River irrigation area. The study was performed in a test field located in Zhengzhou, Henan Province, China. A numerical model of soil water and nitrogen transport for winter wheat under wide ridge and furrow irrigation was created using HYDRUS-2D. The behavior of soil water and nitrogen was predicted for different irrigation water and nitrogen treatments and analyzed to identify pathways of nitrogen transport and transformation. The nitrogen balance was calculated for the different water and nitrogen treatments. The coefficients of determination for measured and predicted values of soil water content, nitrate nitrogen, and ammonium nitrogen in both horizontal and vertical directions were all >0.68; the mean absolute error was