Deep Learning-based Plant Leaf Disease Detection

In this study, three deep learning models (CNN, VGG16, and VGG19) were compared for the objective was to detect plant diseases, and to accomplish this, a dataset comprising 9,127 images of plants annotated with disease labels was used to train and evaluate the model’s using accuracy, F1 score, recall, and precision as performance metrics. The results show that CNN achieved the highest overall performance in the classification task, with an accuracy of 0.97 and an F1 score of 0.95. However, the VGG16 and VGG19 models also demonstrated strong performance, with accuracies of 0.96 and 0.95, respectively. The use of deep learning for plant illness interpretation has many advantages, such as automatically extracting relevant features from the input images and scalability. However, there are also limitations such as overfitting, computational resources, and the need for high-quality annotated data. Additional investigation is necessary to assess the efficacy of deep learning models under different conditions and to develop methods for addressing these challenges.The research discusses the importance of evaluating models using multiple performance metrics to gain a comprehensive understanding of their capabilities. Additionally, it highlights potential areas for future research, including the use of transfer learning techniques, the evaluation of different deep learning architectures, and the use of additional performance metrics. The research further discusses the practical applications of deep learning models in image classification tasks, with a focus on plant leaf disease detection. The results of this research have the capacity to enhance the efficiency and efficacy of identifying and treating diseases in the agricultural sector. In general, this comparative analysis offers valuable perspectives on the functionality of deep learning models for image classification duties, with implications that extend to various sectors.