# Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
## 1 Introduction  
Plant pests and diseases are one of the major problems affecting food security worldwide [1]. Due to the impact of climate change on the incidence of pests and diseases, actions for their protection, e.g., crop monitoring, are mandatory [2]. However, this is a time-consuming and costly task for the farmer [3]. Within the technological solutions for crop monitoring, artificial intelligence (AI) and deep learning (DL) techniques have demonstrated high accuracy values in the detection of pests and diseases [4]. However, challenges remain when using these types of models in natural environments (e.g., variability of colors, shapes, textures, and light) [5], while the number of examples in the image sets used to train these models has not been very large. In turn, their images may contain distractors (e.g., the hand or shoes of the farmer), which could divert the attention of the model into false patterns that affect the decision about the presence of the disease in the plant [6, 7]. In the state-of-the-art, there are numerous solutions for crop disease identification using transfer learning with DL-based models. For example, a GoogLeNet-based model allows the identification of 38 classes of 14 crop species and 26 diseases, under controlled conditions using the Plantville dataset (54,303 images) [8]; the model achieved 99.25% accuracy using weight transfer, and 98.24% accuracy with fine-tuning. However, when the trained model was evaluated with a different dataset, the accuracy was reduced to 31%. Although these differences between the training/validation set and the test set may be related to overfitting, they may also be due to the test set being more complex thanthe training/validation set, or to the fact that the test set is not necessarily more complex but simply different than the training/validation set [9]. Similarly, a pre-trained network based on GoogLeNet was used to classify leaf diseases of 12 plant species (56 classes in total). In that study, the usefulness of a small dataset (i.e., 1383 images) was evaluated, reaching accuracy values for the classes between 65% and 100% [10]. Likewise, the trained model was evaluated with images of the dataset after background removal, obtaining better accuracy values for most of the classes [11]. In another study, a combination of CNN (Convolutional Neural Networks) architectures (specifically VGG16 + Inception) was used for the classification of maize and rice diseases from the Plantvillage dataset (3852 images, 4 categories), and the accuracy obtained was 92% for rice and 80% for maize. Additionally, this hybrid model was compared against other models obtained by transfer learning, whose accuracy results were 65.19% for VGG19, 85.56% for Inceptionv3, 65.19% for ResNet50 and 80.27% for DenseNet [12]. Specifically in coffee leaf monitoring, several pre-trained models (e.g., AlexNet, GoogLeNet, VGG16, ResNet50 and MobileNetV2) have been used for disease identification [13,14]. For instance, DL-based models were trained with a dataset of Arabica coffee leaves affected by 4 types of diseases and 5 levels of affectation (healthy, very low 0.1-5%, low 5.1-10%, high 10.1-15% and very high >15%), obtaining accuracy results between 90% and 95%. However, the authors warn of the need to use images captured in real environments, as they differ from images of controlled environments, for example, in terms of illumination and the presence of shadows [10]. In another study, ResNet was used as the backbone of the model, and two types of datasets were used to train and validate it. The first one corresponds to raw images from the RoCoLe dataset, and the second one corresponds to a mixture between binary segmentation and patches of the original images. According to the results, the model trained with the second dataset provides an accuracy of 98% versus 75% of the model trained with the first dataset [15].