PlantVitGnet: A Hybrid Model of Vision Transformer and GoogLeNet for Plant Disease Identification

Abstract

Diseases are one of the major factors that have the potential to reduce plant production, food security and ultimately humanity's survival. Therefore, timely and correct identification of plant diseases is important in ascertaining methods to control the diseases. This paper focuses on the application of Deep Learning in identifying plant diseases, and the research's recommendation is a combination of the Vision Transformer (ViT) and GoogLeNet architectures. The objective of this work is to combine the strengths of both models so as to attain increased accuracy and faster computation. This proves that the proposed model yields a substantial accuracy of 99.20% a, 99.30% precision and 99.10% recall. F1-score shows the highest performance compared to several state-of-the-art models. For comparison, the Vision Transformer, better known as ViT, attained a 97.80% accuracy, 97.90% precision, 97.70% recall and 97.80% F1 scores, and GoogLeNet attained 98. 60% accuracy, 98. 70% precision, 98.50% recall and 98.60% F1-score. The present hybrid model substantially enhances the capacity to identify plant diseases, hence providing a comprehensive means of managing the early diseases in the plantations. Due to high performance in the desired indicators, it is applicable for real-world purposes, controlling crops and increasing their yields.