Automated irrigation of apple trees based on dendrometer sensors

Abstract

This study evaluates the efficiency of an automated irrigation system using dendrometer sensors in apple orchards and compares it to a standard grower commercial irrigation approach based on soil moisture sensors. An algorithm was developed to balance daily stem shrinkage (water loss) and expansion (water uptake), aiming for a stable dendrometer signal. The dendrometer-based irrigation system (DENDRO) significantly reduced water use—by 38 % in 2022 and more than 45 % in 2023—while maintaining yields similar to those of the soil moisture-based system (SOIL). The DENDRO responded quite well to plant water stress, as indicated by stem water potential (WP). Although the tested algorithm proved to be efficient, the results also indicated the potential for optimization. One example is shortening the averaging period used to calculate stem recovery (R Δ ). The SOIL method was effective in fruit production but proved to be less efficient in reflecting water needs. Alternative approaches, including FAO-based irrigation (FAO) and a linear regression model combining dendrometer parameters and climatic data (MODEL), were also assessed. The FAO method tended to overestimate water requirements, while the MODEL method showed promise for dynamic irrigation adjustment based on climatic conditions and dendrometer values. Overall, the findings highlight the advantage of integrating plant-based sensors, such as dendrometers, for more precise irrigation management in orchard systems, leading to more sustainable water use without compromising crop yield. • Dendrometers enable precise, sustainable orchard water use. • DENDRO-based irrigation saved 38–45 % water while maintaining apple yields. • SOIL-based irrigation produced more fruit but did not reflect water needs. • Stem water potential around −1.6 MPa has not been harmful in this experiment.

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