A Hybrid Model Approach for Advanced Disease Identification in Precision Agriculture

This paper describes the in-depth study performed using various machine leaning models and showcases the power of Convolution Neural Networks (CNN) in image classification. Exploring machine learning architectures such as Generic CNN, ResNet50, EfficientNet V2, VGG16, DenseNet121, and the Hybrid Model, the study prioritizes Validation Accuracy. The hybrid model surpasses individual benchmark models in accuracy, showcasing its efficacy. This model is accessed in the form of a Flask Web Application. By integrating the strengths of the Hybrid model along with various other techniques to provide better results, our research endeavors to offer an efficient, diverse and accurate tool for disease identification.