Basal Stem Rot (BSR), a disease caused by Ganoderma boninense (G. boninense), has posed a significant concern for the oil palm industry, particularly in Southeast Asia, as it has the potential to cause substantial economic losses. The breeding programme is currently searching for G. boninense-resistant planting materials, which has necessitated intense manual screening in the nursery to track the progression of disease development in response to different treatments. The combination of hyperspectral image and machine learning approaches has a high detection potential for BSR. However, manual feature selection is still required to construct a detection model. Therefore, the objective of this study is to establish an automatic BSR detection at the seedling stage using a pre-trained deep learning model and hyperspectral images. The aerial view image of an oil palm seedling is divided into three regions in order to determine if there is any substantial spectral change across leaf positions. To investigate if the background images affect the performance of the detection, segmented images of the plant seedling have been automatically generated using a Mask Region-based Convolutional Neural Network (RCNN). Consequently, three models are utilised to detect BSR: a convolutional neural network that is 16 layers deep (VGG16) model trained on a segmented image; and VGG16 and Mask RCNN models both trained on the original images. The results indicate that the VGG16 model trained with the original images at 938 nm wavelength performed the best in terms of accuracy (91.93%), precision (94.32%), recall (89.26%), and F1 score (91.72%). This method revealed that users may detect BSR automatically without having to manually extract image attributes before detection.
Oil palm is the world’s most important oil crop, accounting for roughly 40% of all traded vegetable oil. Basal Stem Rot (BSR) has posed a significant concern to the oil palm industry, particularly in Southeast Asia, as it has the potential to cause substantial economic losses. Laboratory-based methods are reliable for early BSR detection. However, they are costly and destructive. Other methodologies used a semi-automatic approach which requires human intervention. Therefore, this paper presents an automatic detection of BSR using hyperspectral data and a deep learning approach, which includes a Mask R-CNN for image segmentation and a VGG16 as a classifier. The Mask R-CNN was trained using Set B images, and the images in Set A were masked using the mask produced by the Mask R-CNN. The VGG16 was trained with the masked images (Set A). This fully automatic approach demonstrated high model performance with 85.46% accuracy, 86.74% F1 score, 95.02% recall, and a classification time of 0.08s/image. The findings of this research have the potential to significantly benefit the oil palm industry by automatically detecting BSR at an early stage, thus allowing for the prevention of disease spread. It can also help solve the problem of labor shortage.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.