Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.

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Ficus deltoidea is a popular herbal plant as ethnomedicine, especially from its leaves. The decoction of leaves is used as tonic to regain energy, strengthen uterus, improve blood circulation, treat diabetes, gout, hypertension and also to reduce water in lung disease. Therefore, the plant physiology including its photorespiration mechanism is of great importance to understand its biological properties. Plant proteins are building blocks of many bioactive secondary metabolites. The present study extracted the plant proteins using Tris-buffered phenol technique, and then crude proteins were separated by gel electrophoresis prior to peptide identification using LC-QTOF MS. The identified proteins were used to explain the C1-metabolism and photorespiration in F. deltoidea. Mass spectra of peptides were found to match 229 proteins, and 9 of them were strongly related to C1-metabolism. The proteins such as pentatricopeptide repeat protein, tetratricopeptide repeat protein, 5,10-methylenetetrahydrofolate dehydrogenase:5,10-methenyltetrahydrofolate cyclohydrolase and folylpolyglutamate synthase are essential in photorespiratory cycle. The detection of the proteins suggests that F. deltoidea perform photorespiration via C1-THF synthase/SHMT pathway which is the alternative photorespiratory pathway. The findings of this study could be used to explain the production of bioactive metabolites in F. deltoidea. This is also the first report to reveal the C1-metabolism and photorespiration in F. deltoidea.