Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages.

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods.

Identification of winter wheat pests and diseases based on improved convolutional neural network.

Wheat pests and diseases are one of the main factors affecting wheat yield. According to the characteristics of four common pests and diseases, an identification method based on improved convolution neural network is proposed. VGGNet16 is selected as the basic network model, but the problem of small dataset size is common in specific fields such as smart agriculture, which limits the research and application of artificial intelligence methods based on deep learning technology in the field.

Nanocomposite of Half-Fin Anchovy Hydrolysates/Zinc Oxide Nanoparticles Exhibits Actual Non-Toxicity and Regulates Intestinal Microbiota, Short-Chain Fatty Acids Production and Oxidative Status in Mice.

The nanocomposite of half-fin anchovy hydrolysates (HAHp) and zinc oxide nanoparticles (ZnO NPs) (named as HAHp(3.0)/ZnO NPs) demonstrated increased antibacterial activity compared to either HAHp(3.0) or ZnO NPs as per our previous studies.

[Artificial neural networks for the identification of infrared spectra of Ilex Kudingcha].

In order to identify Ilex Kudingcha, two kinds of models of artificial neural networks (ANN), i.e. competitive neural network and back propagation neural network, were used to analyze their infrared spectra.