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Statistical models for predicting potential laying pattern were important for economically optimal breeding strategy of egg production in a poultry flock. The aim of this study was to establish an optimal model for describing egg production using room temperature, feed consumption, layer weight, and age during the production period. The following mathematical models were used PSO-LSSVM (Particle swarm optimization-Least squares support vector machines) and PCA (Principal component analysis). The daily recorded of egg production data from 19,666 laying-type hens was used. Hen-daily egg production was described using egg-laying rate during successive days after reaching sexual maturity (120 days of age) and daily recorded room temperature, feed consumption, layer weight, and age. Then present study used PCA to study the correlation between this data. Using the Pearson correlation coefficient of the five factors (maximum and minimum shed temperatures, layer weight, feed consumption, and age) and egg-laying rate, the present study weighted each factor according to its influence on the egg-laying rate. In addition, LSSVM was used to create a regression model of the weighted data, and PSO was to optimize parameters (penalty coefficient c and kernel parameter g) in the LSSVM. Our experimental results showed that the goodness-of-fit criteria value (MSE) was small, lower than that of existing prediction models. The PSO-LSSVM model was able to fit well egg-laying rate of the whole Hy-Line Brown laying-type hens' flock.

It is of great practical significance to quickly, accurately, and effectively identify the effects of rice diseases on rice yield. This paper proposes a rice disease identification method based on an improved DenseNet network (DenseNet). This method uses DenseNet as the benchmark model and uses the channel attention mechanism squeeze-and-excitation to strengthen the favorable features, while suppressing the unfavorable features. Then, depth wise separable convolutions are introduced to replace some standard convolutions in the dense network to improve the parameter utilization and training speed. Using the AdaBound algorithm, combined with the adaptive optimization method, the parameter adjustment time reduces. In the experiments on five kinds of rice disease datasets, the average classification accuracy of the method in this paper is 99.4%, which is 13.8 percentage points higher than the original model. At the same time, it is compared with other existing recognition methods, such as ResNet, VGG, and Vision Transformer. The recognition accuracy of this method is higher, realizes the effective classification of rice disease images, and provides a new method for the development of crop disease identification technology and smart agriculture. [ABSTRACT FROM AUTHOR]

The evaluation of rice disease severity is a quantitative indicator for precise disease control, which is of great significance for ensuring rice yield. In the past, it was usually done manually, and the judgment of rice blast severity can be subjective and time-consuming. To address the above problems, this paper proposes a real-time rice blast disease segmentation method based on a feature fusion and attention mechanism: Deep Feature Fusion and Attention Network (abbreviated to DFFANet). To realize the extraction of the shallow and deep features of rice blast disease as complete as possible, a feature extraction (DCABlock) module and a feature fusion (FFM) module are designed; then, a lightweight attention module is further designed to guide the features learning, effectively fusing the extracted features at different scales, and use the above modules to build a DFFANet lightweight network model. This model is applied to rice blast spot segmentation and compared with other existing methods in this field. The experimental results show that the method proposed in this study has better anti-interference ability, achieving 96.15% MioU, a speed of 188 FPS, and the number of parameters is only 1.4 M, which can achieve a high detection speed with a small number of model parameters, and achieves an effective balance between segmentation accuracy and speed, thereby reducing the requirements for hardware equipment and realizing low-cost embedded development. It provides technical support for real-time rapid detection of rice diseases. [ABSTRACT FROM AUTHOR]

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. [ABSTRACT FROM AUTHOR]