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Sunday Dogo is a Lecturer I in the Department of Agronomy at Federal University, Gashua, Yobe, Nigeria. His academic background is associated with agronomy and agricultural education. His contribution addresses the application of machine learning to precision agriculture and smart farming systems.
Precision agriculture and intelligent farming are revolutionising traditional farming methods through the use of machine learning (ML), Internet of Things (IoT) technology, remote sensing, robotics, and data-informed decision-making. Conventional farming methods frequently depend on standardised management techniques that may overlook the regional heterogeneity of soil properties, crop development, water resources, pest prevalence, and weather patterns. Machine learning facilitates the examination of diverse agricultural data to aid with localised decision-making, resource efficiency, crop surveillance, and yield forecasting. This chapter elucidates the essential principles of precision agriculture and intelligent farming, explores significant agricultural data repositories, and analyses prevalent machine learning and deep learning methodologies. It presents a cohesive framework that links field sensors, remote sensing platforms, data processing systems, predictive models, and apps for farmers. The section additionally addresses model assessment, implementation obstacles, data compatibility, interpretability, economic viability, and ecological sustainability.