A Hybrid Machine Learning Approach Utilizing CNN Feature Extraction with Traditional Classifier to Identify Strawberry Leaf Diseases

Strawberry production is globally significant because it contains high nutrients. Strawberry leaf disease shapes a significant barrier to strawberry cultivation worldwide. Numerous strawberry leaf diseases can impede the growth of strawberry plants. Leaf Scorch is one of the dangerous strawberry leaf diseases caused by the fungus "Diplocarpon Ear-liana." Strawberry leaf disease diagnosis by traditional methods requires a lot of effort and time and is also challenging to identify accurately. In order to increase strawberry production, it is therefore essential to automatically detect strawberry leaf diseases. Machine learning and Deep learning have the potential to detect strawberry leaf diseases automatically. In this paper, we proposed a hybrid Machine Learning methodology using CNN feature extraction with the traditional ML classifier to detect Strawberry Leaf Diseases. For this instance, we used the strawberry leaf dataset from the PlantVillage Updated dataset. Based on our research, ResNet50 with Support Vector Machine diagnoses strawberry leaf scorch with 99.80% accuracy. This research assured sustainable advancements in strawberry production by integrating and offering an effective way to detect strawberry leaf diseases.