Deep Learning Models for Accurate Potato Disease Diagnosis and Forecasting

Abstract

Potato cultivation plays a vital role in global food security and is a significant source of income for farmers worldwide. However, the widespread occurrence of potato diseases poses a substantial threat to crop yield and quality. Early detection and accurate prediction of diseases are crucial for timely intervention and effective management of potato crops. In this research paper, we propose a novel deep learning approach for potato disease prediction, leveraging the capabilities of neural networks to enhance accuracy and efficiency. The research begins by compiling a comprehensive dataset of diverse potato disease samples, including various pathogens and environmental conditions. This dataset is used to train and validate deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), tailored to the specific characteristics of potato diseases. To facilitate real-world application, a user-friendly mobile application is developed, enabling farmers and agricultural experts to easily access the predictive models and diagnose potential diseases in their potato crops. The application harnesses the power of edge computing, allowing predictions to be made locally on mobile devices without relying on cloud-based services. To assess the performance of the proposed deep learning approach, we compare it with traditional machine learning techniques and human experts' assessments. Our results demonstrate that the deep learning models outperform conventional methods and show remarkable accuracy in predicting various potato diseases. Furthermore, we explore the potential of transfer learning to enhance the robustness of the models across different potato varieties and geographic regions. The transfer learning approach fine-tunes pre-trained models on limited data from new regions, showcasing promising results in disease prediction for previously unseen samples. In conclusion, our research highlights the potential of deep learning in revolutionizing potato disease prediction and crop management practices. The proposed approach presents an efficient and accurate means for early disease detection, aiding farmers in making informed decisions to mitigate potential crop losses and enhance overall productivity. By providing a technological advancement to the agricultural sector, this study contributes to sustainable potato cultivation and food security on a global scale.