Abstract: The present invention discloses a control method and equipment for a livestock and poultry health inspection robot for multi-index collection. The method comprises the following steps: S1: performing initial detection on the robot; S2: according to a set inspection time and a target inspection point, generating a target position instruction and sending the target position instruction to the robot; S3: controlling the robot to reach a target inspection point according to the target position instruction in the step S2; S4: controlling the robot to collect multi-index data at the target inspection point reached in the step S3; and S5: judging whether the inspection of the set target inspection point is completed; if yes, returning to a charging pile; otherwise, jumping to the step S2. The present invention can control the robot to automatically complete data collection tasks at a fixed time and a fixed point.

Abstract: The present invention discloses a method and a device for non-destructive sorting of fertilization information of hatching eggs before incubation. The method comprises the following steps: obtaining a first time sequence feature of hatching eggs and collecting spectrum data of the hatching eggs according to the first time sequence feature; preprocessing the spectrum data; selecting the preprocessed spectrum data to obtain a feature band; constructing a hatching egg classification neural network model and training the hatching egg classification neural network model; classifying the hatching egg information according to the feature band by using the trained hatching egg classification neural network model to obtain fertilization information; and obtaining a second time sequence feature of the hatching eggs, and sorting the hatching eggs. The present invention improves the precision of detecting the fertilization information of hatching eggs.

Abstract: The present disclosure provides a method for estimating a body size and weight of a pig based on deep learning, and relates to the technical field of deep learning. The present disclosure predicts the weight of the pig by using a convolutional neural network. Relevant features are learned by the convolutional neural network, and feature engineering extraction is not needed to be established, so that extracted features are more comprehensive, and the convolutional neural network is superior to a linear model in processing of noisy data and nonlinear problems of data. Images of the pig are shot by an ordinary two-dimensional (2d) color camera.