Smartphone‐Based Monitoring Identifies the Importance of Farm Size and Soil Type for Coffee Tree Productivity at a Large Geographic Scale

ABSTRACT Smartphone‐based monitoring has been increasingly applied to coffee crops for multiple tasks, such as predicting coffee tree productivity. However, its implementation remains limited to small‐scale use, typically at the individual plant level. At larger scales, such as the farm level, its application is largely unexplored. Moreover, it is unclear whether the use of smartphone‐based monitoring can help identifying key factors driving coffee tree productivity such as climate, soil, and management characteristics. To address these challenges, we investigate coffee tree productivity at the farm level and its key driving factors using smartphone‐based monitoring and explainable artificial intelligence (xAI), and compare the results with those obtained from manual monitoring at the farm level. We used a multimodal data set composed of satellite data (soil and climate), smartphone‐based monitoring (coffee tree productivity), and management characteristics (area, shade trees, and farm shape). The results showed that smartphone‐based monitoring reached a of R ² = 0.84 in predicting coffee tree productivity at the farm level. The xAI results revealed that both smartphone‐based and manual monitoring approaches identified the coffee cultivation area (greater than 13 ha) and soil texture (sandy, clay loam) as the most important variables influencing coffee tree productivity at farm level. The analysis also indicated that shade trees do not significantly affect coffee tree productivity. These findings suggest that smartphone‐based monitoring can serve as a reliable and scalable alternative to manual monitoring for evaluating coffee tree productivity at the farm level.