Sustainable Waste Management Practices in Animal Husbandry for Biofertilizers and Renewable Energy Production Using Random Forest Classification Model

Abstract

The development of the production of animal products has increased the generation of livestock waste dramatically, posing significant environmental, agricultural, and human health problems due to poor waste management practices that result in greenhouse gases, water pollution, soil erosion and pathogen transmission. Waste management has thus become a necessity towards sustainable livestock farming that promotes renewable energy production and organic production of fertilisers. The proposed research will consider an intelligent and sustainable waste management system wherein a random forest classification model is used to categorize livestock waste by its potential to be used in the production of biofertilizers and generation of renewable energy. The proposed system makes use of key waste parameters like the moisture content, pH level, organic carbon concentration, nitrogen content, temperature, volatile solids, ratio of carbon to nitrogen, volume of the waste, and generation of methane to carry out a smart classification and prediction. Random Forest algorithm is also used because of its high classification accuracy, strong, lower ability to overfit, and ability to deal with nonlinear agricultural data. The framework classifies livestock wastes into various categories such as high biofertilizers potential waste, high renewable energy potential waste, medium-efficiency waste, and low-efficiency waste in order to facilitate optimal usage of the wastes and recovery of the resources sustainably. Through experimental analyses, it has been established that the proposed model shows a good performance in terms of classifications and enhances decision making in the context of renewable energy production, organic nutrient recycling, and environmentally sustainable livestock management. The suggested structure plays a major role in minimizing environmental pollution, increasing the efficiency of biogas production, increasing the efficiency of organic fertilizer production, and providing AI-based smart farming apparatuses to promote sustainable rural and agricultural growth.