Abstract
A comprehensive, multidisciplinary analysis of the latest developments in digital agriculture is conducted with the use of artificial intelligence (AI), machine learning (ML), and the Internet of Things. By automation and the use of modern, scalable technology solutions that reduce risks, support sustainability, and give farmers predictive advice, traditional agricultural processes are being updated and improved to maximize production. In this paper, the applications of AI, IoT, and ML in agricultural production systems are discussed in detail. The applications that have been explored can be broadly categorized into three areas: soil management, livestock management, and crop management. Weed detection, disease identification, and yield forecasting are some of the applications for crop management. Two applications of livestock management are animal welfare and production. The use of AI, IoT, and ML will make it possible to collect data from agricultural activities for analysis and the extraction of insightful knowledge, facilitating prompt and accurate decision making to increase agricultural productivity. This will result in farming that is more exact and efficient while requiring less labour and producing high-quality produce.
Recommended Citation
Macheso, P. S. (2024). Comprehensive Survey on Applications of Internet of Things, Machine Learning and Artificial Intelligence in Precision Agriculture. Tanzania Journal of Engineering and Technology (TJET), 42(4), 30-45. https://doi.org/https://doi.org/10.52339/tjet.v42i4.922
Publisher Name
University of Dar es Salaam