# Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing
## 1 Introduction  
Hop (_HumulusInputs_ L.) is a plant native to the Northern Hemisphere, whose infloresances, called cones, have palatable and antibacterial properties. Due to its presence of alpha and beta acids and essential oils, hop is mainly used for brewing beer, bringing flavor, bittermess, and aroma to the drink [1, 2, 3]. Although Brazil is ranked as the third-largest beer producer globally, with an annual output of approximately 13.3 billion litters, most of the hops used in the process are imported from major global producers such as Germany and the United States [4]. Despite efforts to cultivate hop varieties adapted to warm climates, the cultivated area remains small [5], and significant gaps persist in scientific research regarding its management, production, and quality in tropical climates. Nevertheless, the cultivation of hop has been expanding in Brazil, with indications that the country experiences a growing harvest each year [6]. Conditions such as Brazil's predominant soil type, latosol [7], extended periods of sunlight, and the climatic requirements of hop cultivation [1] create a favorable environment for crop development in the country. However, another essential factor for the healthy growth and commercial production of hop is fertilization, particularly nitrogen supplementation. This nutrient is crucial for primary growth, playing a structural function in amino acid synthesis. These characteristics accelerate plant growth, promoting cone flowering [8]. Studies have shown that nitrogen application positively affects early hop development [9], which can also influence cone quality by altering acid content, total oils, dry matter, overall yield, and cone color [10; 11]. However, excessive nitrogen application can lead to excessive plant growth and predispose the plant to a greater risk of leaf diseases and insect infestations [1]. At the same time, nitrogen availability is reduced due to urea volatilization and leaching, which may compromise plant development. Therefore, monitoring the available nitrogen levels is essential, and can be carried out through leaf analysis. This method assesses the leaves and verifies the response of the plant's metabolism to a shortage, sufficiency, or excess of this nutrient [12]. Furthermore, biochemical analyses in the laboratory are not always carried out by producers due to high costs or limited access to testing facilities. This highlights the need for alternative nutritional monitoring methods. The use of digital image processing (DIP) computational tools, such as RGB (Red, Blue, and Green) analysis, offers a lower-cost and time-efficient alternative for assessing plant nutritional status. In addition, compared to visual assessment, it offers greater precision, improving operational and financial capacity [13; 14]. Studies have shown that classification models generated via machine learning techniques outperform human evaluation in determining the geometric parameters in leaf images [15]. In the context of hop cultivation, image processing was used by [PERSON] et al. [16] to classify hop species, presenting the first study of image processing and hop variety classification in Brazil. In this work, they developed a classification model based on 12 varieties and 1592 leaf images, achieving an accuracy of 95.69%. However, the application of these technologies remains largely unexplored in hop cultivation, especially in Brazil, where the literature is scarce and still primarily exploratory and observational. Multivariate statistical and machine learning techniques have been used to classify nutritional deficiencies and phytopathologies using leaf image processing [14; 17; 18]. Among the statistical models used, discriminant analysis, both linear (LDA) and quadratic (QDA), has been successfully applied. LDA was one of the models used by [PERSON] et al. [15], achieving the second-highest accuracy in the experiment, as well as the fastest performance in distinguishing the quality of grape leaves damaged by black rot, even though it misclassified four leaves.