Leaf Detectives: A Deep Learning Approach to Grapevine Varietal Identification with DenseNet-121

Effective viticulture depends on accurate identification of grapevine leaves, which shapes vine care and grape quality. This article presents a novel automated approach for grapevine leaf categorization utilizing DenseNet-121, a state-of-the-art convolutional neural network distinguished for image recognition performance. Essential for the wine and fruit sectors, grapevines (Vitis vinifera) show a variety of leaf traits vital for precise varietal identification. These leaves require an effective automated solution since manual classification of them is labor-intensive and prone to mistakes. In this work, we classify five different types of high-resolution grapevine leaf photos from a dataset: Ak, Ala Idris, Buzgulu, Dimnit, and Nazli. Resized to 256x256 pixels, the photos are handled to produce a batch size of 32 and a classification accuracy of 96%. There are five main parts to the approach: analysis; model architecture construction; preprocessing and dataset collecting; model training and evaluation. Resizing and normalizing photos helps the Kaggle-derived dataset to be pre-processed so standardizing inputs. DenseNet-121 is chosen for its effective feature extraction and dense connection; its design is modified for the classification purpose by changing the last layer. To improve model generalization, training comprises categorical cross-entropy loss, the Adam optimizer, and data augmentation methods. Validation and test datasets help to evaluate performance; metrics including accuracy and F1-score direct model improvement. The findings confirm that DenseNet-121 greatly increases grapevine leaf identification accuracy and efficiency, hence lowering manual labor and raising classification consistency. This work provides important new perspectives for next developments in precision agriculture and highlights the possibilities of deep learning technology in viticulture.