Title: Image-Based Strawberry Leaves Classification Using Deep Convolutional Neural Networks
Authors: Amal Dwimah, Samy S. Abu-Naser
Volume: 9
Issue: 6
Pages: 64-69
Publication Date: 2025/06/28
Abstract:
Strawberry is a widely cultivated crop across the globe and plays a vital role in the agricultural economy. However, strawberry plants are susceptible to a range of diseases, especially those affecting the leaves. Early detection of such diseases is critical for successful intervention and maximizing yield. With the rise of deep learning, particularly convolutional neural networks (CNNs), image-based disease detection has become a promising area of research. In this study, we present a CNN model for classifying strawberry leaf images into four distinct categories, including healthy and diseased samples. Using a dataset of 3,742 images, the model was trained and evaluated through structured experiments. Results show that the model achieved a validation accuracy of 87.5%, confirming its effectiveness. Visual analyses further demonstrate the learning process and features extracted by the model. This research is intended to support future development of automated tools and mobile applications in the field of precision agriculture.