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To better explore the encapsulation efficiency of citral microcapsules through spray‐drying, amphiphilic methylcellulose (MC) was used as the emulsifier and the interior wall material, with chitosan (CTS) and alginate (ALG) as the composite external wall materials. The sequence and proportion of wall materials were compared and optimized based on the stability of the emulsions and citral content in the microcapsules. MC/CTS/ALG was the best wall material for citral microencapsulation. Formulation comprising 1.5 ml citral, 0.8 g MC, 1.0 g CTS, and 6.0 g ALG had the best entrapment of citral, resulting in citral unit content of 46.4 mg/g and the encapsulation efficiency of 82.2%. Scanning electron microscope and transmission electron microscopy images expressed distinct spherical core–shell structure of the microcapsules. Citral microcapsules absorbed water during storage, which resulted in particle size increase and cracks in the microcapsules surface. Water absorption properties in common used environments (open in air, sealed plastic bag, glass bottle) were investigated. CoCl2 microcapsules were used to intuitively express the water absorption through the color change. The results are referential for industry to select proper wall materials and storage conditions for spice microcapsules to increase the shelf life of relevant foods.

Recently, the attention mechanism combining spatial and channel information has been widely used in various deep convolutional neural networks (CNNs), proving its great potential in improving model performance. However, this usually uses 2D global pooling operations to compress spatial information or scaling methods to reduce the computational overhead in channel attention. These methods will result in severe information loss. Therefore, we propose a Spatial channel attention mechanism that captures cross-dimensional interaction, which does not involve dimensionality reduction and brings significant performance improvement with negligible computational overhead. The proposed attention mechanism can be seamlessly integrated into any convolutional neural network since it is a lightweight general module. Our method achieves a performance improvement of 2.08% on ResNet and 1.02% on MobileNetV2 in top-one error rate on the ImageNet dataset.

Dissolved oxygen content is a key indicator of water quality in aquaculture environment. Because of its nonlinearity, dynamics, and complexity, which makes traditional methods face challenges in the accuracy and speed of dissolved oxygen content prediction. As a solution to these issues, this study introduces a hybrid model consisting of the Light Gradient Boosting Machine (LightGBM) and the Bidirectional Simple Recurrent Unit (BiSRU). Firstly, Linear interpolation and smoothing were used to identify significant parameters. LightGBM algorithm then determines the significance of dissolved oxygen by eliminating irrelevant variables and predicting dissolved oxygen in intensive aquaculture. Finally, the attention method was implemented to map the weighting and learning parameter matrices, so enabling the BiSRU's hidden states to be assigned different weights. The findings shown that the presented prediction model can accurately anticipate the fluctuating trend of dissolved oxygen over a 10-day period in just 122 seconds, and the accuracy rate reached 96.28%. Comparing the model effects of LightGBM -BiSRU, LightGBM -GRU, LightGBM-LSTM, and BiSRU -Attention takes the least time. Its higher prediction accuracy can provide an essential reference for intensive aquaculture water quality regulation.

Accurately predicting humidity changes in sheep barns is important to ensure the healthy growth of the animals and to improve the economic returns of sheep farming. In this study, to address the limitations of conventional methods in establishing accurate mathematical models of dynamic changes in humidity in sheep barns, we propose a method to predict humidity in sheep barns based on a machine learning model combining a light gradient boosting machine with gray wolf optimization and support-vector regression (LightGBM–CGWO–SVR). Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model. To avoid the local extremum problem, the CGWO algorithm was used to optimize the required hyperparameters in SVR and determine the optimal hyperparameter combination. The combined algorithm was applied to predict the humidity of an intensive sheep-breeding facility in Manas, Xinjiang, China, in real time for the next 10 min. The experimental results indicated that the proposed LightGBM–CGWO–SVR model outperformed eight existing models used for comparison on all evaluation metrics. It achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083 in terms of mean absolute error, root mean square error, mean squared error, and normalized root mean square error, respectively, and a maximum value of 0.9973 in terms of the R2 index.

The pigeon food production industry from breeding to processing into food for market circulation involves many stages and people, which is prone to food safety issues and difficult to regulate. To address these problems, one possible solution is to establish a traceability system. However, in traditional traceability systems, a number of stages involved and each of them provides their own data accumulated in the database. Therefore, complex traceability data are compose of too many stages easily result in confusing information for customers. Besides, centralized data storage makes data vulnerable to be tampered with. To solve these problems, hazard analysis and critical control points (HACCP) principles have been utilized in our work which is a comprehensive traceability system. In this work, we analyze the pigeon food production industry through HACCP principles and determine some critical control points (CCPs), including incubation, breeding, transportation, slaughtering, processing, and logistics. With the help of these CCPs, we are able to build a traceability system with critical and abundant data but not too complicated for users. To further improve the system, there are different kinds of techniques integrated into it. Firstly, a permissioned blockchain, Hyperledger Fabric, is selected as blockchain module to enhance trustworthiness of data. Secondly, the system contains various IoT devices for automatically collecting environmental parameter data with the aim of reducing human interference. Besides, it is worth mentioning that the proposed information management device can decrease the data entry burden. Consequently, the implementation of the traceability system increase consumers’ confidence in pigeon food production. To summarize, it is a new application of modern agricultural information technique in food safety and a bold experiment in the field of poultry, particularly pigeons.

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In this paper, the dissipative particle dynamics (DPD) method is used to simulate the self-assembly process, appearance, mesoscopic structure and wrapping properties of microcapsules formed with citral as the core material and chitosan and sodium alginate as single wall materials, and with citral as the core material and chitosan-sodium alginate, chitosan-methylcellulose, sodium alginate-chitosan and sodium alginate-methylcellulose as double wall materials. The effects of chitosan content and wall material composition on the structure, morphology, encapsulation performance and stability of microcapsules are compared and analyzed. In addition, the microcapsules are deeply analyzed by using the mesoscopic structure, radial distribution function and diffusion coefficient. This study provides a new idea and method for the preparation of citral microcapsules and is of great significance for the design and development of new composite wall microcapsules.