Precision agriculture for water saving: The case of processing tomato and table grape
Giuseppe Ferrara, Alessandro Pesole, Rita De Marco, Sara Bisceglie, Giovanni Popeo, Simone Pascuzzi, Luigi TedoneSocietal Impact Statement
Agriculture faces increasing pressure to produce high‐quality food while reducing water use under intensifying climate change and water scarcity. This study compared sensor‐based precision irrigation, drone monitoring, and near‐infrared spectroscopy with conventional farmer management in table grape and processing tomato production systems. Precision agriculture improved crop water status, photosynthetic performance, yield, and marketable quality, while reducing irrigation by 8%–15% in table grape and approximately 15% in processing tomato. These findings support wider adoption of integrated digital tools through targeted incentives and farmer training, offering a scalable strategy to strengthen long‐term water security, farm resilience, and sustainable food production globally.
Summary
The study aimed to evaluate the effectiveness of precision agriculture (PA) technologies, specifically sensor‐based irrigation and drones, on table grape (cv.
Allison
) and processing tomato (cv.
Taylor
) production compared to traditional farming methods (control).
The research involved field experiments in two locations in the Puglia region, southeastern Italy, in 2023 and 2024. For table grape and processing tomato, two different irrigation managements (PA vs. Control/Farmer) were compared, monitoring physiological, morphological, yield, and quality parameters. For processing tomato, drone imagery, and ground measurements were also conducted. Predictive models for fruit ripeness and quality traits of both species were also developed using near‐infrared (NIR) spectroscopy data, preprocessing techniques, and PLS regression. For table grape, the PA vines showed greater water potential stability, more uniform stomatal conductance, and higher chlorophyll content, resulting in higher and more consistent production with 8%–15% water savings. For processing tomato, PA management improved plant vegetative indicators, total and marketable yields, and reduced water consumption by approximately 15%. Three out of four calibrated models using NIR showed predictive performance suitable for future practical applications. The findings highlight the potential of PA to improve water resource utilization, crop development, yield, and fruit quality, contributing to more sustainable agricultural systems in regions facing water scarcity and climate change. Moreover, this work demonstrates how sensor‐driven irrigation of the two crops (with more equilibrated plants) directly influenced high‐accuracy predictive modelling. The integration of environmental sensors (for irrigation) and optical sensors (for quality) will represent the core of modern smart farming.