August 11, 2025

Agro-Detect: A Cnn Driven Early Detection of Leaf Diseases

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Analysis using transfer learning identifies plant diseases with 98% accuracy, suggesting effective diagnostic solutions in agriculture.

Overall test accuracy reached 98%, demonstrating a high efficacy in detecting plant diseases effectively.
This analysis identified 38 plant disease categories, employing a robust model based on ResNet50 architecture.
Utilizing transfer learning from ImageNet, models were fine-tuned for enhanced accuracy in disease classification.
Results highlight the significance of CNNs and transfer learning for improving agricultural diagnostics and preventing crop losses.

Vyas et al. (2025) studied this question.

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