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Precision agriculture represents a transformative approach to modern farming, leveraging advanced technologies to optimise agricultural production management. This farming approach has several applications to improve productivity; for example, drones have been used to monitor crop health and detect water stress, enabling timely interventions to reduce losses. Additionally, GPS-guided tractors have optimized planting and harvesting operations in crops like soybeans, allowing for more precise and efficient use of resources. This paper presents a comprehensive review of 57 studies focused on the application of precision agriculture technologies for crop protection and soil health. Three key aspects were examined: the technologies employed, the outcomes achieved, and the barriers to adoption. Among the technologies, spectral imaging emerged as the most widely used for early detection of plant stress, diseases, and pests, followed by machine learning algorithms, UAVs (Unmanned Aerial Vehicles), and IoT (Internet of Things) devices, all of which enable real-time monitoring and targeted interventions. In terms of soil health, sensor-based technologies, GIS (Geographic Information Systems), and Variable Rate Application (VRA) systems were the most common tools for assessing soil properties and managing inputs. Despite the promise of these technologies, significant barriers hinder widespread adoption, including high costs, skill gaps, limited awareness, and resistance to change. This review highlights the need for continued innovation and coordinated efforts across research, policy, and education to overcome these barriers and support the global transition to more sustainable agricultural practices.
Mamabolo et al. (Thu,) studied this question.