###### Abstract  
Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. Therefore, this study aims to identify leaves with excessive nitrogen using one-dimensional convolutional neural networks (1D-CNN) on a dataset of spectral data using the Keras library. Seeds of cucumber were planted in several pots and, after growing the plants, they were divided into different classes of control (without excess nitrogen), N\({}_{30\%}\) (excess application of nitrogen fertilizer by 30%), N\({}_{60\%}\) (60% overdose), and N\({}_{60\%}\) (90% overdose). Hyperspectral data of the samples in the 400-1100 nm range were captured using a hyperspectral camera. The actual amount of nitrogen for each leaf was measured using the Kjeldahl method. Since there were statistically significant differences between the classes, an individual prediction model was designed for each class based on the 1D-CNN algorithm. The main innovation of the present research resides in the application of separate prediction models for each class, and the design of the proposed 1D-CNN regression model. The results showed that the coefficient of determination and the mean squared error for the classes N\({}_{30\%}\), N\({}_{60\%}\) and N\({}_{90\%}\) were 0.962, 0.0005; 0.968, 0.0003; and 0.967, 0.0007, respectively. Therefore, the proposed method can be effectively used to detect over-application of nitrogen fertilizers in plants. nitrogen prediction; 1D convolution neural networks; cucumber; crop yield improvement 4.0/. +
Footnote †: journal: Medicalcomponent of chlorophyll. However, nitrogen deficiency/overdose in plants is one of the major nutritional problems that is affecting current agricultural practices. The first sign of nitrogen deficiency is the paleness of old leaves, because this element can move from the lower old leaves to the upper young leaves. The leaves are usually yellowish green and light yellow due to the lack of chlorophyll formation. Late red and reddish-purple growth is observed as a result of anthocyanin dye formation; also, the leaves, stems and branches become thin in nitrogen deficiency status [2; 3; 4]. Unfortunately, in order to increase yield, farmers have resorted to excessive use of nitrogen fertilizers and are not very willing to use biological, organic and micronutrient fertilizers [5]. However, studies indicate that nitrate accumulation in agricultural products does not play a very positive role in sustainable production and public health [6; 7]. Excessive consumption of nitrogen makes the plant susceptible to diseases and pests. In fruit crops, it causes damage to flowers and blossoms, and reduces the quality of the product. If the main source of nitrogen supply is ammonium, it can poison the plant, as a result of which the vascular tissues are damaged and the leaves appear as cups. Therefore, at the present stage, scientific management of production and consumption of fertilizers is inevitable to improve the structure of production and the level of public health by improving the optimal use of fertilizers. Many farmers perform leaf analysis when they observe signs of food disturbance or unknown symptoms on the leaves. Leaf analysis helps growers better manage nutrient inputs by determining the type and amount of fertilizer needed and identifying problems that may lead to poor crop performance; but, at that time, it is evident that the plant has been damaged. Therefore, in order to get a better result, disorders must be identified before the onset of symptoms. Leaf analysis is time consuming and requires special methods to interpret the data correctly [8; 9]. On the other hand, this method is destructive. Thus, the application of non-destructive and fast methods is very important.