Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks

Abstract

Potato crops are vital to global food security and economy, yet they are vulnerable to a wide range of leaf diseases that can significantly impact yield and quality. Rapid diagnosis and accurate identification of these disorders are critical for effective disease control and prevention. In this research, we offer an extensive evaluation and contrast of three state -of-art CNN models- VGG19, DenseNet121 and ResNet50-in order to identify and forecast potato leaf diseases. Our study employed a sizable dataset of potato leaf images, containing diverse healthy and afflicted specimens, to train and assess the performance of the chosen CNN models. Extensive data augmentation techniques were employed to enhance the dataset’s diversity and generalization capabilities. We evaluated the models considering their accuracy, precision, recall, F1-score and computational efficiency to determine the most fitting model for real-life applications. The results demonstrate that all three CNN models achieved high performance in identifying and predicting potato leaf diseases, with VGG19 emerging as the top performer followed closely by DenseNet121 and ResNet50.Our findings provide valuable insights into the efficacy of DL approaches for potato leaf ailment identification and offer a foundation for future research and deployment of these models in precision agriculture systems. Ultimately, this work aims to support the development of more robust and efficient tools for timely disease diagnosis, enabling farmers and agronomists to make better-informed decisions and safeguard the health and productivity of potato crops worldwide.