Any kind of compassionate thoughts can't be expressed through words, but it appears on their facial expression. So, the facial expression reveals the emotions of individuals. The recognition of such emotions can be understood correctly or sometimes ambiguously from the opponent. Hence, there is a scope for automatic facial expression recognition (FER) in the context of image processing. The FER system has three different phases: face detection, feature extraction and expression classification. In face detection phase, Viola Jones face detector is used to crop the original image such that only the face region is retained by removing the unwanted region. In feature extraction stage, High-order Joint Derivative Local Binary Pattern (HJDLBP) and Local Binary Pattern (LBP) histogram algorithms are used for extracting features from the cropped image. In last stage, Support Vector machine (SVM) classifier is used in finding the precise facial expression.CK+ dataset has been used for training and testing, which consist of 442 image samples. We have considered six different universal possible expressions such as, happy, anger, disgust, fear, surprised, and sad for identification. The experimental results indicate that the overall accuracy of the proposed system was 74.8%, which is high compare to the results available in literature.

One of the technologies, artificial intelligence (AI), requires quick adoption in the livestock sector. The use of AI technology can be highly beneficial in a number of key areas in the livestock business, including monitoring, forecasting, optimising the growth of farm animals, combating parasites, biosecurity threats, and diseases, and monitoring farm animals and farm management. Livestock farms will be helped by artificial intelligence to gather and analyse data in order to precisely forecast consumer behaviour, including purchasing patterns, leading trends, etc. Farms will be able to automate procedures, cut significant expenses, and enhance the quality of livestock byproducts like milk with increased investment.

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