# Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
## 1 Introduction
The solutions proposed in the literature for the classification of coffee leaf diseases (Rust, Phoma, Cercospora, Leaf Miner an others) have used a diversity of hyperparameters that do not facilitate specific conclusions. In the case of the optimizer, SGD [14,16,17,18], Adam [18,19], or RMSprop [18] have been used, both with fixed learning rates ([0.01, 0.001][17,20]) and learning rate values selected from a range ([0.001-0.3], [0.01-0.00001][14,18]). The number of units in the fully connected layer, prior to the classification layer, was also implemented with values in the range 20-200 [14], or 256-1024 [21]. Dropouts of 0.5 [18], or 0.8 [19] have also been applied. These models have been trained from 20 to 300 epochs [16,17,19,20,21], with batch sizes including 16, 32 or 64 images [17,19,21]. Regarding the architecture types, models with specific design [17] and models by transfer learning have been evaluated, including MobileNetV2 [19], DenseNet [19], InceptionResNetV2 [19], ALexNet [20], GoogLeNet [17], VGG16 [21], ResNet50 [16,17,19], and Xception [21]. Also, fine tuning [18] or data augmentation [21] have been involved. In summary, the state-of-the-art works have assumed the values of hyperparameters for both architecture (e.g., network type, network depth, neurons in fully connected layers) and training (e.g., optimizer, learning rate), but have not performed an exhaustive evaluation of their impact on model performance. Neither have they proposed a concrete methodology to rank the hyperparameters and make decisions on their values for the in-crop disease detection problem. For this reason, this study proposes a model-centered methodology for image classification tasks (specifically crop disease detection) that allows answering the following questions: what types of hyperparameters are the most influential on the classifier performance? and how to organize an experiment that allows selecting the best values of these hyperparameters? The main contributions of this study include the following results:  
* We propose a methodology for model-centric deep learning projects that allows hierarchization of hyperparameters and selection of the most appropriate values for the performance of crop disease detection models. * This methodology was applied to the problem of rust detection in coffee crops and allowed to rank five types of hyperparameters (type of network, depth, number of neurons in the FC layer, optimizer and learning rate), as well as to identify values that allow to improve classifier performance. * With this methodology, it was possible to improve the F1-score of the classifier from 18.4% to 92.71%, without making any adjustments to the image set. The remainder of the study is organized as follows. Section 2 explains preliminary concepts related to convolutional neural networks (CNN) and transfer learning. Section 3 is focused on the materials and methods of the current study, specifically the coffee leaf rust dataset and the proposed methodology to identify the hyperparameters with the greatest impact on the performance of coffee rust classification models. Section 4 shows the results of the study, and Section 5 is the discussion of the research. Finally, Section 6 concludes the study.