Retrospection On the Applications of Machine Learning Techniques in The Digital Agricultural Management - Rachana Jaiswal (AG Volumes, 2022)
Agriculture is currently experiencing a period of transition. Farmers are faced with the challenges of increasing food production despite a changing climate and an expanding population, all while adjusting to new technologies that have irrevocably changed agriculture. These challenges are compounded by the fact that agriculture has been forever transformed by technological advancements. A new era of digital agriculture management has been ushered in as a result of the application of machine learning to numerous facets of agriculture, such as the scheduling of irrigation and the control of insects. This article explores agricultural application cases for machine learning and deep learning, which may assist farmers in confronting these challenges head-on. Depending on the specific needs of each agricultural application, a variety of machine learning methods could be developed. It will be beneficial for data scientists to have a high level of understanding about use cases and the machine learning technologies that are linked with them.