Precision Pig Farming Image Analysis Using Random Forest and Boruta Predictive Big Data Analysis Using Neural Network and K- Nearest Neighbor

Conditions and monitoring for production are significant issues in livestock accuracy Agriculture, in which image measurement and smart data collection are required. Dynamical surveillance and review of this Article Man are
suggested as a device for scientific identification and growth evaluation of pigs. The Watershed enhanced algorithm is adapted to each human animal's section in chronic occlusion, depending on the depth of the photos captured during flight Camera in the chosen area of interest. For swine's weight, the rate of development is calculated from the image-based calculations and predicted using a segmented linear fitting form. Related results will then be used to interpret and explain incidents. As real-time feedback to the farmers, it happens in the pig hen. Preliminary studies have demonstrated a high potential for precision farming methods for livestock farming to increase efficiency and animal health. In this paper, Machine Learning is used in IMAGE analysis using Random forest and boruta with Predictive Big Data analysis on the pig farming data using the neural network and k- nearest neighbor algorithm for advanced predictive data analysis of our pig farming agriculture. Keyword: Big data; Random forest; boruta; neural network