# Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing
## 4 Discussion  
One of the visual symptoms of a decreased nitrogen concentration in leaves is chlorosis, the insufficient production of chlorophyll by the plant, which results in a color transition from light green to yellow, depending on the severity [40]. Thus, this factor may be related to the significance of the green color in both databases. Similarly, in some plant species, such as tomato (_Solanum lycopersion_), cauliflower (_B. oleraca_ var. botrytis), cabbage (_Brassica oleracea_), and _Arabidopsis thaliana_, nitrogen deficiency induces anthocyanin production, which may be associated with the significance of blue and red colors in the average of the groups [41]. For the low-resolution image database (mobile), LDA models demonstrated higher sensitivity and balanced accuracy compared to QDA models. Additionally, as evidenced by the Box's M test, the LDA models outperformed the QDA models, with accuracies of 73.4% and 69.4%, respectively, for the model using only the significant variables as predictors. In [PERSON] et al. 's study [15], LDA also achieved the highest accuracy in classifying the health status of grape leaves among the models studied, with an accuracy of 98.99%. Furthermore, in a study involving classification methods for identifying and quantifying damaged pixels in leaves, the linear discriminant analysis classifier demonstrated the best performance, with an average precision of 95%, in addition to being a computationally efficient and robust model [42]. Similarly, the LDA model for the mobile database starting from set 1 exhibited a considerable classification capacity for leaves with a nitrogen concentration above the threshold, using only two precursor variables (G and B). In another study on the early detection of excess nitrogen application in tomato leaves using a non-destructive hyperspectral imaging system, when comparing the performance of nine machine classifiers, LDA showed the second-lowest performance, with a correct classification rate (CCR) of 85.5% [43]. However, this rate was still close to that of other classifiers with superior performance, including hybrid artificial neural networks, independent component analysis, harmony search, the bee algorithm, and deep learning-based classifiers using convolutional neural networks, which had CCRs between 88.8% and 91.6%. Additionally, LDA had a CCR 27% higher than the other classic supervised classifier, support vector machines (SVMs). In the model adjusted using the predictors selected using the VIF from the set containing all the predictors (set 2), QDA showed higher accuracy than LDA, but lower balanced accuracy, due to differences in group sizes between the two groups. For the higher-resolution data (scanner), QDA models performed better in both cases, except for specificity in the models for set 1, with 84.6% for LDA compared to 75.6% for QDA. High-resolution images significantly enhance insect detection models' accuracy, as they provide better detail and clarity, which is crucial for identifying small pests such as whiteflies on tomato leaflets. On the other hand, low-resolution images reduce accuracy, highlighting the importance of training models on images that match the data resolution. Models trained on mixed-resolution datasets performed comparably to those trained exclusively on high- or low-resolution images, suggesting that incorporating multiple resolutions can be beneficial [44, 45]. Both databases yielded models with classification rates for group 2 exceeding 80% specificity, the highest being 88.3% for the lower-resolution (mobile) images and 84.6% for the higher-resolution (scanner) images. Additionally, for higher-resolution images, the QDA model generated from set 2 exhibited a classification rate of 71.8% for leaves with nitrogen levels above the threshold. The study by [PERSON] and [PERSON] [17] also reported a higher overall accuracy rate for the quadratic discriminant analysis model. On the other hand, most models had lower sensitivity than specificity, especially those trained on lower-resolution (mobile) images.