Abstract
Plant diseases significantly impact agricultural productivity, making it essential to accurately assess not only the presence of disease but also its severity. This paper presents a deep learning-based framework using ResNet50 to predict plant disease severity across multiple levels (Healthy, Mild, Moderate, and Severe). The system is designed to process plant leaf images, perform augmentation to improve robustness and classify disease severity with high accuracy. The proposed method leverages labelled and pseudo- labelled datasets to enhance the model's performance through data augmentation and training on unlabelled images. The results demonstrate the model's effectiveness in distinguishing between varying levels of disease progression, providing valuable insights for early intervention and optimized crop management strategies. This platform is crucial for automating plant disease severity monitoring, and aiding decision-making processes for timely interventions in agriculture.
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Claims 1
Claim · model-written summary
The ResNet50 framework classified plant disease severity with high accuracy across severity levels.
Verbatim
e0“process plant leaf images”
e1“classify disease severity with high accuracy”
e2“distinguishing between varying levels of disease progression”