This objective of the study was to develop a model for the determination of N deficiency in cherry trees using a combination of visible near infrared methods and spectro-radiometric measurement.In our experimental design,cherry seedlings were grown under various N deficiency conditions in nutrient-controlled containers.The reflectance values of plant leaves were measured using a spectro-radiometer.Plant leaves samples were simultaneously collected.Their nutrient contents were determined in the laboratory.Afterwards,we performed a statistical comparison of the reflectance values.Sample analysis results established the significant wavelengths.Moreover,we received accurate regression models for predicting N deficiency in cherry leaves that were grown in nutrient solutions.Next,we verified the model validity by measuring the reflectance of the leaves collected from cherry orchards at various locations using a spectroradiometer.Nutrient deficiencies were calculated using the developed model,and then,the predicted and measured data were compared to evaluate model validity.From these results,we determined the wavelengths that yielded the most accurate results for N prediction,selected from the blue and green regions of the spectrum.We established that for N prediction in cherry trees,the simplest model can be created using 560and 570nm wavelengths.However,the evaluated model can be applicable only under certain conditions.We concluded that in order to develop aprediction method with sufficient application capacity,as well as the ability to assess nutritional and physiological characteristics,the ecology condition of the plant should be properly considered based on the model. This objective of the study was to develop a model for the determination of N deficiency in cherry trees using a combination of visible near infrared methods and spectro-radiometric measurement. Our experimental design, cherry seedlings were grown under various N deficiency conditions in nutrient- controlled containers. The reflectance values ​​of plant leaves were measured using a spectro-radiometer. Plant leaves samples were also collected. The nutrient contents were determined in the laboratory. Afterwards, we performed a statistical comparison of the reflectance values. Sample analysis results established the significant wavelengths. Moreover, we received accurate regression models for predicting N deficiency in cherry leaves that were grown in nutrient solutions .Next, we verified the model validity by measuring the reflectance of the leaves collected from cherry orchards at various locations using a spectroradiometer. Nutrient deficiencies were calculated using the developed model , and then, the predicted and measured data were compared to evaluate model validity. Fighted these results, we determined the most accurate results for N prediction, selected from the blue and green regions of the spectrum. We established that for N prediction in cherry trees, the simplest model can be created using 560 and 570 nm wavelengths. However, the evaluated model can be applicable only under certain conditions. we said that in order to develop aprediction method with sufficient application capacity, as well as the ability to assess nutritional and physiological characteristics, the ecology condition of the plant should be properly considered based on the model.