Zinc (Zn) deficiency associated with low dietary intake is a well-documented public health problem, resulting in serious health and socioeconomic problems. Field experiments were conducted with wheat to test the role of both soil and foliar application of ZnSO4 in Zn concentration of whole grain and grain fractions (e.g., bran, embryo and endosperm) in 3 locations. Foliar application of ZnSO4 was realized at different growth stages (e.g., stem elongation, boot, milk, dough stages) to study the effect of timing of foliar Zn application on grain Zn concentration. The rate of foliar Zn application at each growth stage was 4 kg of ZnSO4·7H2O ha(-1). Laser ablation (LA)-ICP-MS was used to follow the localization of Zn within grain. Soil Zn application at a rate of 50 kg of ZnSO4·7H2O ha(-1) was effective in increasing grain Zn concentration in the Zn-deficient location, but not in the locations without soil Zn deficiency. In all locations, foliar application of Zn significantly increased Zn concentration in whole grain and in each grain fraction, particularly in the case of high soil N fertilization. In Zn-deficient location, grain Zn concentration increased from 11 mg kg(-1) to 22 mg kg(-1) with foliar Zn application and to 27 mg kg(-1) with a combined application of ZnSO4 to soil and foliar. In locations without soil Zn deficiency, combination of high N application with two times foliar Zn application (e.g., at the booting and milk stages) increased grain Zn concentration, on average, from 28 mg kg(-1) to 58 mg kg(-1). Both ICP-OES and LA-ICP-MS data showed that the increase in Zn concentration of whole grain and grain fractions was pronounced when Zn was sprayed at the late growth stage (e.g., milk and dough). LA-ICP-MS data also indicated that Zn was transported into endosperm through the crease phloem. To our knowledge, this is the first study to show that the timing of foliar Zn application is of great importance in increasing grain Zn in wheat, especially in the endosperm part that is the predominant grain fraction consumed in many countries. Providing a large pool of Zn in vegetative tissues during the grain filling (e.g., via foliar Zn spray) is an important practice to increase grain Zn and contribute to human nutrition.
Three-dimensional LiDAR systems that capture point cloud data enable the simultaneous acquisition of spatial geometry and multi-wavelength intensity information, thereby paving the way for three-dimensional point cloud recognition and processing. However, due to the irregular distribution, low resolution of point clouds, and limited spatial recognition accuracy in complex environments, inherent errors occur in classifying and segmenting the acquired target information. Conversely, two-dimensional visible light images provide real-color information, enabling the distinction of object contours and fine details, thus yielding clear, high-resolution images when desired. The integration of two-dimensional information with point clouds offers complementary advantages. In this paper, we present the incorporation of two-dimensional information to form a multi-modal representation. From this, we extract local features to establish three-dimensional geometric relationships and two-dimensional color relationships. We introduce a novel network model, termed MInet (Multi-Information net), which effectively captures features relating to both two-dimensional color and three-dimensional pose information. This enhanced network model improves feature saliency, thereby facilitating superior segmentation and recognition tasks. We evaluate our MInet architecture using the ShapeNet and ThreeDMatch datasets for point cloud segmentation, and the Stanford dataset for object recognition. The robust results, coupled with quantitative and qualitative experiments, demonstrate the superior performance of our proposed method in point cloud segmentation and object recognition tasks.
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