ABSTRACT. One of the most important uses of DNA markers is cultivar identification. However, no DNA fingerprint analysis strategy is available for making DNA markers helpful in practical plant cultivar identification, especially for the identification of a large number of cultivars. We developed a manual cultivar identification diagram strategy for efficient identification of plant cultivars, from which a cultivar identification diagram (CID) of genotyped plant individuals can be constructed manually. This CID could be used as a reference for quick identification of plant cultivars of interest. We used 11-mer RAPD primers to amplify DNA samples of 32 ornamental peach genotypes; all the cultivars were well distinguished by fingerprints from 6 primers. The utility of this CID was verified by identification of three randomly chosen groups of cultivars among the 32 ones that we selected. This CID generated will be useful for the identification of commercially important ornamental peach cultivars.

The general Convolutional Neural Networks (CNNs) have been in practice, being the most conventional algorithm for image-based detection and classification. But over the years, after extensive use of CNN algorithms with different architectures, it has been shown that CNN tends to lose details and features of the image. This led to the use of Capsule-based neural networks for image detection and classification. On the other side, CNN has evolved and integrated with another type of neural network called the Graph Neural Network (GNN). Many existing systems have drawbacks such as feature loss and computation efficiency. Several transfer learning models have been introduced to solve these problems by modifying the existing models and adding different combinations of layers and hyper parameters. However, they still don't provide a clear solution as they are just derived algorithms. Therefore, there is a need to design an algorithm and technique that approaches the image classification process in a unique and different way. This is where the CAPSGNN algorithm comes into use. This proposed model uses the best features of all the other algorithms and fuses them into one algorithm. This reduces the computation time and solves the feature loss problems. Now, reports can be generated faster and more accurately for assisting the process of disease diagnosis in hospitals and saving doctors' time spent on reviewing every report. These speeds up the cycle of the medical field, as the identification of diseases takes more time than the actual treatment and needs to be processed faster for faster treatment and recovery.