VisionaryX-9: Efficient plant disease prediction model

Agriculture, which is the foundation of worldwide sustenance, is facing substantial obstacles in the twenty-first century, mainly because of increasing demands for food production and the growing menace of plant diseases. Ensuring the well-being of crops is of utmost importance for the security of global food supply, which requires the timely and precise detection of plant diseases. Conventional approaches are becoming less effective in dealing with the changing agricultural dangers, necessitating the development of new and creative solutions. The incorporation of machine learning and deep learning technologies in recent agricultural practices has significantly transformed plant disease diagnosis, providing remarkable levels of accuracy, efficiency, and scalability. This study utilises advanced algorithms such as ResNet, VGG, and EfficientNet to improve the accuracy of disease identification, thereby revolutionising precision agriculture. The main objective of this article is to enhance the process of plant disease detection with the help of a sophisticated model, VisonaryX-9, that can not only properly identify plant diseases but also classify their symptoms. The objective is to empower farmers by furnishing them with timely information, enabling informed decisions that directly influence agricultural output and, consequently, global food production. Our contributions consist of the creation of VisonaryX-9, a hybrid model that merges the ResNet and VGG architectures. The model is validated with a dataset of having 87,295 real-world plant disease images. The results obtained indicates the the model outperforms other baseline model with an accuracy of 99.12%. Our objective is to reveal profound understandings that will enhance the discussion on sustainable and resilient farming methodologies.