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.

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.