Ensemble-Based Corn Leaf Disease Classification Using Deep Learning Models

In this chapter, we present a novel ensemble technique for the classification of corn leaf diseases using leaf images. Leveraging the strengths of ensemble learning, our method employs a voting mechanism to effectively identify various diseases afflicting corn leaves. This approach integrates three advanced deep learning models—VGG19, ResNet152, and DenseNet121—to enhance the robustness and accuracy of disease classification. We utilize the PlantVillage leaf disease dataset for both the training and testing phases of our proposed detection system. Through comprehensive experimentation, our ensemble model achieves a remarkable classification accuracy of 99.48% on the test dataset. This significant performance underscores the efficacy of our approach in accurately diagnosing corn leaf diseases, thereby enabling timely interventions to reduce crop losses. The proposed ensemble technique not only improves classification accuracy but also enhances the model's reliability and generalization capability, making it a promising solution for automated crop disease detection systems. This chapter will delve into the methodology behind our ensemble approach, detailing the integration of the deep learning models and the implementation of the voting technique. Additionally, we will discuss the results of our experiments, highlighting the superior performance of our ensemble model compared to individual models. The chapter will also explore the implications of our findings for the development of more reliable and efficient crop disease detection systems, emphasizing the potential benefits for agricultural practices and crop management.