Cucumber disease classification using Transfer Learning

Cucumber farming faces substantial challenges due to various diseases affecting plant health and yield.Rapid and accurate disease detection is crucial for effective management and mitigation strategies.In recent years, machine learning techniques, particularly deep learning models, have shown promise in automated disease diagnosis in plants.Transfer learning, a technique where a pre-trained model is adapted to a new task with limited data, has emerged as a practical solution for agricultural disease classification tasks.This study investigates the effectiveness of transfer learning in cucumber disease classification using convolutional neural networks (CNNs).Additionally, we explore the impact of dataset size and the choice of pre-trained model on classification performance.