Big Data, AI, and Geoinformatics for Sustainable Agriculture: An Introduction

Abstract

Agriculture contributes significantly to economic growth. Agriculture digitization is a major source of concern and a contentious motif around the world. The world’s population is rapidly booming, and with it comes higher demand for food and income. The conventional farming practices were insufficient to satisfy these goals. As a result, new automated approaches were proposed. These new methods met food demands while simultaneously providing career opportunities for people around the world. This chapter is to provide a short explanation of the current incarnation of automation in agriculture. Agriculture entails a variety of processes and phases, the preponderance of which are carried out manually. AI can help with the most challenging and routine chores by supplementing existing technology. When integrated with other technology, it can gather and evaluate big data on a digital platform, determine the best course of action, and even execute that action. Artificial intelligence (AI) simplifies agricultural tasks, such as irrigation, weeding, and spraying, using sensors and other devices integrated in robots and drones. These technologies reduce the use of water, pesticides, and herbicides, maintain soil fertility, and aid in the efficient use of manpower, thus increasing productivity and improving quality. This should be done while maintaining agriculture sustainability and combating environmental constraints such as climate change, water scarcity, and the risk of increasing erosion and productivity owing to extreme weather events. Precision agriculture (PA) arose from technological advancements in the 1980s, leading to the advent of vital technologies such as GPS and satellite photography.