Apple Variety Classification: Leveraging CNN and Random Forest for Precision

An important difficulty in computer vision and agricultural technology is the automatic classification of apple cultivars based on their shape, texture, and colour properties. In this article, we suggest a unique method for tackling this problem that combines Random Forest classifiers and Convolutional Neural Networks (CNNs). In total, 5,510 high-resolution photos of the five well-known apple cultivars Gala, Cortland, Pink Lady, Honeycrisp, and Fuji were gathered in our extensive collection. In the first step of our process, a CNN automatically extracts distinguishing features from these photos. Four convolutional layers with ReLU activation make up the CNN architecture, which is followed by layers with maximum pooling to reduce spatial dimension. The Random Forest classifier then uses the output probabilities from the CNN to determine the final classification. We rigorously experimented to show the accuracy and resilience of our model in identifying these apple types. Deep learning and ensemble approaches work together to successfully capture minute variations in shape, texture, and colour. The agriculture and food industries stand to benefit significantly from this research in terms of automated quality control, precise inventory management, and increased customer satisfaction. Our method opens up new directions for future study in automated fruit classification and agricultural automation while demonstrating the potential of contemporary machine learning techniques to simplify difficult agricultural operations.