METHODS AND RESULTS: This study aimed to isolate and characterize the full-length cDNA encoding ERG11 from G. boninense. The G. boninense ERG11 gene expression during interaction with oil palm was also studied. A full-length 1860 bp cDNA encoding ERG11 was successfully isolated from G. boninense. The G. boninense ERG11 shared 91% similarity to ERG11 from other basidiomycete fungi. The protein structure homology modeling of GbERG11 was analyzed using the SWISS-MODEL workspace. Southern blot and genome data analyses showed that there is only a single copy of ERG11 gene in the G. boninense genome. Based on the in-vitro inoculation study, the ERG11 gene expression in G. boninense has shown almost 2-fold upregulation with the presence of oil palm.
CONCLUSION: This study provided molecular information and characterization study on the G. boninense ERG11 and this knowledge could be used to design effective control measures to tackle the BSR disease of oil palm.
RESULTS: In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep-learning-based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. Six traditional feature-based approaches and six deep-learning feature-based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers, namely a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature-based approaches performed better than the traditional feature-based approaches. The best accuracy of 92.10% was obtained with the SVM classifier.
CONCLUSION: A deep-learning feature-based approach could capture the details and characteristics between different types of chili pests and diseases even though they possessed similar visual patterns and symptoms. © 2020 Society of Chemical Industry.