Abstract
Global Coconut (Cocos nucifera (L.)) cultivation faces significant challenges, including yield loss, due to pest and disease outbreaks. In particular, Weligama Coconut Leaf Wilt Disease (WCWLD) and Coconut Caterpillar Infestation (CCI) damage coconut trees, causing severe coconut production loss in Sri Lanka and nearby coconut-producing countries. Currently, both WCWLD and CCI are detected through on-field human observations, a process that is not only time-consuming but also limits the early detection of infections. This paper presents a study conducted in Sri Lanka, demonstrating the effectiveness of employing transfer learning-based Convolutional Neural Network (CNN) and Mask Region-based-CNN (Mask R-CNN) to identify WCWLD and CCI at their early stages and to assess disease progression. Further, this paper presents the use of the You Only Look Once (YOLO) object detection model to count the number of caterpillars distributed on leaves with CCI. The introduced methods were tested and validated using datasets collected from Matara, Puttalam, and Makandura, Sri Lanka. The results show that the proposed methods identify WCWLD and CCI with an accuracy of 90% and 95%, respectively. In addition, the proposed WCWLD disease severity identification method classifies the severity with an accuracy of 97%. Furthermore, the accuracies of the object detection models for calculating the number of caterpillars in the leaflets were: YOLOv5-96.87%, YOLOv8-96.1%, and YOLO11-95.9%.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
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Claims 6
Claim · model-written summary
The method identified Weligama Coconut Leaf Wilt Disease with 90% accuracy.
Verbatim
e0“The results show that the proposed methods identify WCWLD and CCI with an accuracy of 90% and 95%, respectively.”
t0“Coconut Leaf Wilt Disease”
Claim · model-written summary
The method identified coconut caterpillar infestation with 95% accuracy.
Verbatim
e0“The results show that the proposed methods identify WCWLD and CCI with an accuracy of 90% and 95%, respectively.”
t0“Coconut Caterpillar Infestation”
Claim · model-written summary
The leaf wilt severity method classified severity with 97% accuracy.
Verbatim
e0“the proposed WCWLD disease severity identification method classifies the severity with an accuracy of 97%.”
t0“Coconut Leaf Wilt Disease”
Claim · model-written summary
YOLOv5 counted caterpillars in leaflets with 96.87% accuracy.
Verbatim
e0“the accuracies of the object detection models for calculating the number of caterpillars in the leaflets were: YOLOv5-96.87%, YOLOv8-96.1%, and YOLO11-95.9%.”
t0“Coconut Caterpillar Infestation”
Claim · model-written summary
YOLOv8 counted caterpillars in leaflets with 96.1% accuracy.
Verbatim
e0“the accuracies of the object detection models for calculating the number of caterpillars in the leaflets were: YOLOv5-96.87%, YOLOv8-96.1%, and YOLO11-95.9%.”
t0“Coconut Caterpillar Infestation”
Claim · model-written summary
YOLO11 counted caterpillars in leaflets with 95.9% accuracy.
Verbatim
e0“the accuracies of the object detection models for calculating the number of caterpillars in the leaflets were: YOLOv5-96.87%, YOLOv8-96.1%, and YOLO11-95.9%.”
t0“Coconut Caterpillar Infestation”