The impact of machine learning driven approach on agriculture and food industry: The case of a developing country

The agriculture and food sector perform a fundamental function in human existence and also the economic well- being of any nation. This paper examines the concept of machine learning approach as it relates to the agriculture and food sector, its applications, also the impact on economic growth in developing countries. The 21st century era is faced with challenges like explosion in population, resource constraints, and climate change which are prevalent in developing countries. However, there are opportunities in this technological advancement era with heightened precision and innovation on the improvement of food safety, processing, quality for sustainability in the food ecosystem. Despite these advancements and opportunities there are barriers in the form of obtaining high scalable quality datasets, integrating multisensory inputs for food quality assessment, and meeting the demands for Graphics Processing Units (GPUs) for the optimization of deep learning models for food imaging. This paper is analyzing the application of machine learning in agriculture and food sector with a view to revolutionizing the traditional way of farming practices by leveraging on the technology for increased efficiency, environmental sustainability, better productivity in farming practices and improved food safety standards. We consider the integration of ethics in this study to address the concerns that has to do with privacy that associate with the collection and processing of data. Furthermore, stakeholders, scholars, practitioners, and policy makers can use it as a guide to increase research to navigate the ever-evolving technology-infused innovative agriculture that will shape the path to a robust, efficient and sustainable agri-food sector.