Aiming at breaking down the bottleneck problems of different scale of poultry farms, the low profitability of poultry farming, and backward information management in China, a safe and efficient information management system for poultry farming was designed. This system consists of (1) a management system application layer, (2) a data service layer, and (3) an information sensing layer. The information sensing layer obtains and uploads production and farming information through the wireless sensor network built in the poultry house. The use of a cloud database as an information storage carrier in the data service layer eliminates the complex status of deploying local server clusters, and it improves the flexibility and scalability of the system. The management system application layer contains many sub-function modules including poultry disease detection functions to realize the visual management of farming information and health farming; each module operates independently and cooperates with each other to form a set of information management system for poultry farming with wide functional coverage, high service efficiency, safety, and convenience. The system prototype has been tested for the performance of wireless sensor network and cloud database, and the results show that the prototype is capable of acquiring and managing poultry farming information.

The grain number on the rice panicle, which directly determines the rice yield, is a very important agronomic trait in rice breeding and yield-related research. However, manual counting of grain number per rice panicle is time-consuming, error-prone, and laborious. In this study, a novel prototype, dubbed the “GN-System,” was developed for the automatic calculation of grain number per rice panicle based on a deep convolutional neural network. First, a whole panicle grain detection (WPGD) model was established using the Cascade R-CNN method embedded with the feature pyramid network for grain recognition and location. Then, a GN-System integrated with the WPGD model was developed to automatically calculate grain number per rice panicle. The performance of the GN-System was evaluated through estimated stability and accuracy. One hundred twenty-four panicle samples were tested to evaluate the estimated stability of the GN-System. The results showed that the coefficient of determination ( R 2 ) was 0.810, the mean absolute percentage error was 8.44%, and the root mean square error was 16.73. Also, another 12 panicle samples were tested to further evaluate the estimated accuracy of the GN-System. The results revealed that the mean accuracy of the GN-System reached 90.6%. The GN-System, which can quickly and accurately predict the grain number per rice panicle, can provide an effective, convenient, and low-cost tool for yield evaluation, crop breeding, and genetic research. It also has great potential in assisting phenotypic research.

Soil porosity, as an essential indicator for assessing soil quality, plays a key role in guiding agricultural production, so it is beneficial to detect soil porosity. However, the currently available methods do not apply to high-precision and rapid detection of soil with a black-box nature in the field, so this paper proposes a soil porosity detection method based on ultrasound and multi-scale CNN-LSTM. Firstly, a series of ring cutter soil samples with different porosities were prepared manually to simulate soil collected in the field using a ring cutter, followed by ultrasonic signal acquisition of the soil samples. The acquired signals were subjected to three kinds of data augmentation processes to enrich the dataset: adding Gaussian white noise, time shift transformation, and random perturbation. Since the collected ultrasonic signals belong to long-time series data and there are different frequency and sequence features, this study constructs a multi-scale CNN-LSTM deep neural network model using large convolution kernels based on the idea of multi-scale feature extraction, which uses multiple large convolution kernels of different sizes to downsize the collected ultra-long time series data and extract local features in the sequences, and combining the ability of LSTM to capture global and long-term dependent features enhances the feature expression ability of the model. The multi-head self-attention mechanism is added at the end of the model to infer the before-and-after relationship of the sequence data to improve the degradation of the model performance caused by waveform distortion. Finally, the model was trained, validated, and tested using ultrasonic signal data collected from soil samples to demonstrate the accuracy of the detection method. The model has a coefficient of determination of 0.9990 for detecting soil porosity, with a percentage root mean square error of only 0.66%. It outperforms other advanced comparative models, making it very promising for application.