Enhancing Food Quality Improvement and Instigate Smart Agriculture Using Artificial Intelligence Technology

Abstract

Agriculture is a critical sector for any nation. Due to the mounting global population and the increasing demand for food worldwide as well as challenges in weather conditions and the availability of water, Artificial Intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Unfortunately, the agriculture market is explosive. AI is emerging in various major classifications in agriculture, namely crop and soil maintenance, predictive based analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further examinations and analyses. Climate change, soil erosion, and biodiversity loss can cripple the business, as are customers’ shifting tastes in food. The natural environment with which farming interacts continues to present its own set of problems. In addition to a growing population, sustainable agriculture is also threatened by urbanization. In recent years, there has been an increased interest in researchers within Smart Agriculture. This is when machine learning applications in agriculture step on the scene. By analyzing real-time sensor data and historical trends, this technology can empower farming decision-making. With artificial intelligence used in agriculture, manufacturers can better predict demand, improve crop yields and reduce food production costs. Machine learning in agriculture can optimize the way food gets to our table and revolutionize one of the most critical sectors of the economy. Nations that have arid climate conditions would be informed how satellite imagery and mapping can assist them in detecting newer irrigation lands to assist their scarce agriculture resources. Some companies make use of AI software in agriculture by utilizing machine learning for various processes. These tools can make a real difference in agricultural productivity and profitability by reducing waste while enhancing product quality