AI BASED GRAPE LEAF CLASSIFICATION FOR INDUSTRY PURPOSE

Grape leaf classification plays a crucial role in viticulture for disease detection, yield estimation, and overall plant health assessment. Traditional methods for grape leaf classification rely heavily on manual inspection, which is time-consuming and often subjective. In recent years, the advent of artificial intelligence (AI) and machine learning techniques has revolutionized agricultural practices by automating various tasks, including leaf classification. This paper presents an AI-based approach for grape leaf classification tailored for industry applications. The proposed system leverages state-of-the-art deep learning architectures, such as convolutional neural networks (CNNs), to automatically classify grape leaves into different categories based on their health status, disease presence, or other relevant characteristics. The process begins with data acquisition, where high-resolution images of grape leaves are captured using digital cameras or drones equipped with high-quality sensors. These images are then preprocessed to enhance their quality and remove any noise that could affect classification accuracy. Subsequently, the preprocessed images are fed into the CNN model, which has been trained on a large dataset of labeled grape leaf images. To ensure robustness and generalization, the CNN model is trained using transfer learning, where a pre-trained network (e.g., ResNet, VGG) is fine-tuned on the grape leaf dataset. Fine-tuning allows the model to adapt its learned features to the specific characteristics of grape leaves, thereby improving classification performance.