Harnessing Convolutional Neural Networks for Strawberry Leaf Disease Classification

Leaf disease is one of the major obstacles to strawberry cultivation, having the most undesirable detriments on plant health, yield, and fruit quality. Thus, early identification of the disease with high accuracy plays an important role in effective management and mitigation. The paper discusses the prospects of CNNs for the classification of strawberry leaf diseases, showing how deep learning methods have been improving since their beginning up to the latest achievements. We evaluate the performance of various CNN architectures in classifying leaves into healthy and diseased, including traditional models such as VGG and AlexNet, to more recent innovations such as MobileNet and EfficientNet. The paper also discusses data preparation and augmentation techniques that enhance model performance, and evaluation metrics used for measuring their accuracy and generalization. It gives an overall review of the existing literature, critically analyzes the strengths and weaknesses of methodologies applied in the present time to suggest further scope of improvement in diseased classification systems and will finally try to enable the strawberry growers with efficient tools for early disease detection.