Deep Learning Approaches For The Detection And Classification Of Potato Leaf Diseases

Abstract

The increasing number of potato leaf diseases offers a danger to global food security, necessitating creative technologies for rapid and precise identification. This work proposes a novel approach leveraging advanced deep learning algorithms with a particular focus on the DenseNet model, which achieved the greatest accuracy of 87% in classifying various potato diseases. This research intentionally picked nine common potato illnesses for this study: Bacteria, Early Blight, Fungi, Healthy, Late Blight, Nematode, Pest, Phytophthora and Virus. The DenseNet model's superior feature extraction capabilities enable more precise and reliable illness diagnosis compared to older methods. By utilizing the DenseNet architecture exploited its potential to capture complex patterns and hierarchical characteristics in the picture data, considerably boosting the model’s diagnostic accuracy. This innovation offers a powerful tool for farmers enabling early identification and successful management of plant diseases. The deployment of this technology aids in minimizing crop losses and boosting yield quality, contributing to more sustainable farming practices. Early and accurate disease diagnosis with this methodology not only mitigates the impact of illnesses on crop productivity but also supports prompt action lowering the need for expensive chemical treatments and limiting environmental damage. This study's conclusions underline the promise of deep learning in changing agricultural diagnostics providing a scalable and efficient approach to strengthening global food security. The integration of such modern technologies into farming methods is crucial for promoting sustainable agriculture, enhancing crop resilience and ensuring economic stability for farming communities globally