# Attention-Based Light Weight Deep Learning Models for Early Potato Disease Detection
## 1 Introduction
### Literature Review  
Initially, researchers and scientists worked on multiple crops using a dataset known as PlantVillage developed by the researchers from the USA and Switzerland [2]. This region-based dataset was used to study crop disease, which can vary due to differences in leaf shape, variety of leaves, and environmental factors [3]. [PERSON] et al. [4] proposed the use of hyperspectral imaging and advanced machine learning algorithms in agriculture. [PERSON] et al. [5] proposed a \(K\)-means clustering technique for multiclass \(SVM\) classification to segregating contaminated leaf sections. [PERSON] et al. [6] combined the \(SVM\) and \(K\)-means clustering with a backpropagation neural network to identify disease in plant leaves. Many imaging-based research projects have been developed with advancements in technologies such as machine learning and deep learning [7; 8]. A deep CNN model was proposed by [PERSON] and [PERSON] [9] to differentiate between healthy and unhealthy leaves of multiple crops. Their model was trained on the PlantVillage dataset and included 38 kinds of crops. [PERSON] et al. [10] combined autoencoders and a CNN to propose a model for detecting crop diseases from multiple crops, specifically tailored to a region by training on the PlantVillage dataset. [PERSON] et al. [11] proposed a model using ResNet50 architecture for disease detection and severity detection. This model was also trained on the PlantVillage dataset and could detect disease in multiple plants. Many researchers have specifically focused on the potato crop using the PlantVillage dataset. [PERSON] proposed a CNN-based model to detect Early Blight (EB), Late Blight (LB), and healthy images [12]. [PERSON] and [PERSON] [13] also proposed a CNN model for detecting \(EB\), \(LB\), and healthy images using the PlantVillage data. [PERSON] et al. [14] utilized 450 images from the PlantVillage dataset to train multiple machine learning models and observed that Random Forest outperformed all other models. [PERSON] et al. [15] used 300 images from the PlantVillage dataset, 100 images each from EB, LB, and healthy classes. They employed the GLCM feature extraction technique to extract the features from the images and implemented an SVM classifier to achieve an overall accuracy of 96%. [PERSON] et al. [16] used pre-trained models like ResNet50, MobileNet, VGG16, and VGG19 for performance evaluation on the potato leaf images of the PlantVillage dataset. The VGG19 architecture initially achieved the highest accuracy of 92.69%. After being fine-tuned, the model's accuracy improved to 97.89%. [PERSON] et al. [17] used a pre-trained DenseNet architecture with additional layers to classify potato blight using images from the PlantVillage dataset. The enhanced model achieved an accuracy of 97.2%, outperforming the basic DenseNet architecture. [PERSON] et al. [18] proposed three models, VGGNet16, RenNet101, and a modified AlexNet, achieving a training accuracy of 99.97%. [PERSON] and [PERSON] [19] proposed a model using ResNet50 for plant disease detection and classification, attaining an accuracy of 98%. [PERSON] et al. [20] presented a technique incorporating data augmentation, data partitioning, and shuffling along with deep learning models like SVM, VGG16, and a CNN, achieving accuracies of 87%, 92%, and 98%, respectively. [PERSON] et al. [21] proposed a model that combines a hierarchical deep learning convolutional neural network with fuzzy local binary patterns for feature extraction. Their model achieved an accuracy of 95.77%. Most of the research has been conducted using the PlantVillage dataset, which was developed for specific geographic regions and climatic conditions. Potato plant diseases can vary globally due to differences in shape, variety, and environmental factors. Additionally, the PlantVillage dataset has a limited number of samples and exhibits an imbalanced distribution across classes. This limitation can impact the generalizability and robustness of disease detection models trained exclusively on these data.