Deep and machine learning in plant variety classification: A systematic global review of image processing approaches

The application of image processing techniques for plant variety identification has grown substantially over the past two decades, driven by the increasing availability of digital plant imagery and global focus on biodiversity conservation. The present work aims to review recent studies and research in agriculture that utilize various machine/deep learning algorithms, image acquisition techniques, image databases, and accuracy assessment methods for plant variety classification. The PRISMA process was followed to systematically review 83 highly-relevant studies on image processing techniques for plant variety identification, published in the last decade (2015–2025). Deep learning algorithms, especially convolutional neural network variants, are predominantly employed and have demonstrated superior accuracy in plant variety identification. Integrating traditional machine learning techniques with optimization algorithms and handcrafted features further enhances model robustness, paving the way for scalable, real-world agricultural applications. Image data acquisition in most studies was conducted either through publicly available image databases or via smartphone-based imaging. Over the past decade, cereal crop varieties have been the most frequently studied in classification research. Overall, this global review offers valuable insights for researchers, practitioners, and policymakers to enhance the efficiency and accuracy of data-driven plant variety classification.