Revolutionizing plant disease diagnosis: Pre-trained models outperform traditional methods

Diagnosing plant disease is essential to meet the world's growing food demand, which is expected to increase with a population of 9.1 billion by 2050. Diseases can reduce crop yields by 20–40%, so early detection is critical. Traditional disease identification methods include expert analysis and machine learning-based image processing. However, the manual approach is inefficient and error-prone, while machine learning, particularly deep learning methods like Convolutional Neural Networks (CNNs), has revolutionized disease detection by extracting detailed image features.