Exploring the Impact of Data Augmentation on Deep Learning Models for Plant Disease Detection in PlantVillage Dataset

Abstract

Plant diseases are a vital part of ensuring the safety of agricultural crops and the protection of food security. With the development of deep learning techniques, it has been able to automate the detection of these diseases. Unfortunately, one of the main challenges that researchers face when it comes to training models for this field is the lack of labeled training data. The study analyzed the effects of the data augmentation on the deep learning models' performance when it came to identifying plant diseases in the PlantVillage dataset. Here focused on two common ailments affecting tomato and potato plants. To address this issue, we trained CNN models on the PlantVillage dataset using different augmentation techniques, such as cropping, rotation, scaling, and flipping. The results of the study were evaluated using various metrics, including accuracy, recall, area under the curve, and F1 score. Data augmentation can improve a deep learning model's performance when it comes to detecting plant diseases. Our study revealed that CNN models, which were trained using the same methods, achieved higher accuracy and recall metrics when compared to the original models. Furthermore, our findings show that cropping techniques, which are commonly used in augmentation, were more effective at improving the models' performance. Even though the training data is limited, data augmentation can still help improve deep learning models' performance when it comes to detecting plant diseases. The use of such techniques could significantly enhance the models' accuracy and speed in identifying plant diseases, which are important for global food security and the agricultural industry.