This study presents a novel, rapid, and non-destructive approach for assessing the nutritional status of apple trees using a newly developed crop scanner system. The system consists of a camera-based scanner and a dedicated app for tablets or smartphones. The scanner device is standalone and supported on a wireless platform, and it uses a dedicated app interface on tablets or smartphones to communicate, receive and analyze image data. Two experiments were designed and carried out in order to evaluate the system's applicability for nitrogen and chlorophyll estimation in apple leaves. We measured apple leaf color parameters using the crop scanner system and determined leaf concentrations of nitrogen and chlorophyll by two different destructive methods. Data analyses showed that color parameters (R, G, B, and RS value) obtained by the system individually had a varying degree of correlation with both nitrogen (correlation coefficients (r) were - 0.6366, - 0.7856, 0.7497, and 0.7493 for R, G, B, and RS value, respectively) and chlorophyll (correlation coefficients (r) were - 0.7970, - 0.8311, - 0.7962, and 0.8307 for R, G, B, and RS value, respectively) concentrations. To improve the system's applicability, we developed an original leaf color index (LCI) based on RGB parameters. The results indicated that this new LCI could be used to estimate both nitrogen and chlorophyll concentrations with improved accuracy (coefficients of determination (R-2) were 0.6624 and 0.7276, respectively). The new LCI could be incorporated into the smartphone-based app interface, making the system more suitable and accurate for nitrogen assessment of apple trees. Furthermore, an inexpensive and easy-to-use leaf color chart (LCC) was developed. A satisfactory agreement was observed between the measured nitrogen concentrations and the estimates obtained by visually comparing colors of sampled leaves with the LCC, showing the usefulness of this simple LCC in estimating nitrogen content in apple leaves. The performance of both the smartphone-based crop scanner system and the simple LCC tool demonstrated their potential applications in nutrient management of apple orchards that can be carried out in a rapid and non-destructive manner.
