The potential of Explainable Artificial Intelligence in Precision Livestock Farming

Abstract

In the discussion on the increasing demand for food, which is to be met by efficient and sustainable increases in productivity, animal welfare is becoming increasingly important. Animal health issues must be identified to prevent epidemics that significantly impact the economic performance of farms or even cause societal harm. The use of cutting-edge technologies (IoT, sensors, Big Data processing, etc.) is increasingly enabling early intervention in livestock farming to curb productivity losses through real-time monitoring, alerts, and decision support. The ubiquity of these technological solutions has enabled stakeholders to create more robust agricultural supply chains, that deliver sustainable nutrition for a growing population. However, the increasing use of Artificial Intelligence (AI), which is responsible for many of the current breakthroughs in Precision Livestock Farming (PLF) and agriculture in general, has meant that modern decision-support solutions have increasingly transitioned toward black box systems. It has become apparent that a gap exists between efforts to develop more advanced machine learning models, and the growing demands for ethical assessment and transparency in agriculture decision-making. Explainable Artificial Intelligence (XAI) is one such solution that could prove effective in overcoming the current limitations of black-box models, by allowing the decision-making process of such models to be explored. Through a literature review, we evaluate the potential of XAI in various agricultural use cases and demonstrate the potential benefits of its application to precision livestock management.