PlantDiseaseNet-RT50: A Fine-tuned ResNet50 Architecture for High-Accuracy Plant Disease Detection Beyond Standard CNNs

TL;DR

PlantDiseaseNet-RT50 is a fine-tuned ResNet50-based deep learning model that achieves around 98% accuracy in automated plant disease detection across multiple crops, improving speed and reliability over traditional methods.

Contribution

The paper introduces PlantDiseaseNet-RT50, a novel architecture with strategic layer unfreezing and advanced training techniques, enhancing plant disease detection accuracy beyond standard CNNs.

Findings

Achieves approximately 98% accuracy, precision, and recall.

Demonstrates the effectiveness of targeted fine-tuning and optimization techniques.

Provides a practical, high-performance tool for agricultural disease diagnosis.

Abstract

Plant diseases pose a significant threat to agricultural productivity and global food security, accounting for 70-80% of crop losses worldwide. Traditional detection methods rely heavily on expert visual inspection, which is time-consuming, labour-intensive, and often impractical for large-scale farming operations. In this paper, we present PlantDiseaseNet-RT50, a novel fine-tuned deep learning architecture based on ResNet50 for automated plant disease detection. Our model features strategically unfrozen layers, a custom classification head with regularization mechanisms, and dynamic learning rate scheduling through cosine decay. Using a comprehensive dataset of distinct plant disease categories across multiple crop species, PlantDiseaseNet-RT50 achieves exceptional performance with approximately 98% accuracy, precision, and recall. Our architectural modifications and optimization protocol demonstrate how targeted fine-tuning can transform a standard pretrained model into a specialized agricultural diagnostic tool. We provide a detailed account of our methodology, including the systematic unfreezing of terminal layers, implementation of batch normalization and dropout regularization and application of advanced training techniques. PlantDiseaseNet-RT50 represents a significant advancement in AI-driven agricultural tools, offering a computationally efficient solution for rapid and accurate plant disease diagnosis that can be readily implemented in practical farming contexts to support timely interventions and reduce crop losses.