Optimizing nitrogen (N) fertilization is increasingly becoming a key issue to maximize productivity and farmers’ income while reducing environmental impact of agricultural productions. Among the most sophisticated approaches to support variable rate N applications, a central role is played by frameworks that integrate satellite images and smart-scouting driven ground estimates of plant N content (PNC) and critical N concentration. Among the approaches to estimate PNC, the smartphone application PocketN demonstrated its suitability for cereals as well as its great integrability within digital platforms. In this study, we developed genotype-specific calibration curves to derive PNC of tomato crops from PocketN readings and we compared the performance of PocketN with the SPAD ones. Five commercial genotypes were grown in two field experiments in Northern and Southern Italy and four PocketN/SPAD readings and sampling events were carried out along the season. The most reliable relationships between PocketN/SPAD readings and PNC values from the laboratory were obtained for the readings carried out on the apical leaflet of the lower leaves of three plants. Mean R² for all genotypes was 0.75 and 0.62 for PocketN and SPAD, respectively. This allows considering PocketN as a suitable tool for PNC estimates in light of its adoption within digital frameworks aimed at transferring precision agriculture principles to operational farming contexts.

优化氮 (N) 施肥正日益成为最大化生产力和农民收入同时减少农业生产对环境影响的关键问题。在支持可变速率 N 应用的最复杂的方法中，核心作用是由整合卫星图像和智能侦察驱动的植物 N 含量 (PNC) 和临界 N 浓度的地面估计的框架发挥作用。在估计 PNC 的方法中，智能手机应用程序 PocketN 展示了它对谷物的适用性以及它在数字平台中的高度集成性。在这项研究中，我们开发了特定于基因型的校准曲线，以从 PocketN 读数中推导出番茄作物的 PNC，并将 PocketN 的性能与 SPAD 的性能进行了比较。在意大利北部和南部的两个田间试验中种植了五种商业基因型，并在整个季节进行了四次 PocketN/SPAD 读数和采样活动。PocketN/SPAD 读数与实验室的 PNC 值之间最可靠的关系是在三种植物下部叶片的顶端小叶上进行的读数。平均 R^{所有基因型的2}分别为 PocketN 和 SPAD 的 0.75 和 0.62。这允许将 PocketN 视为 PNC 估计的合适工具，因为它在旨在将精准农业原则转移到运营农业环境的数字框架中采用。