Understanding the relationships between crop yields, soil properties, weather patterns and input applications is important for optimising agricultural production. Sustainable intensification aims to increase productivity and input-use efficiency while enhancing the resilience of agricultural systems to adverse environmental conditions through improved management and technology. Artificial intelligence (AI) in precision agriculture (PA) enables growers to deploy highly targeted and precise farming practices based on site-specific agro-climatic field measurements. Recent advances in sensing, machine learning (ML) and modelling offer opportunities for novel smart digital technologies to enable sustainable intensification.
Through the review of the newest scientific publications the application of digital technology in crop production experiments was demonstrated in three topics: (i) continues monitoring of crop and soil characteristics, (ii) quantification of spatial and temporal variability of crop response and (iii) forecasting of crop yield by the use of machine learning approaches. It was concluded that the variation analysis and machine learning approaches can help identify and understand the practices that optimise yield.