Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation Using Deep Learning-Based Image Processing Techniques

被引:0
作者
Vidhanaarachchi, Samitha [1 ]
Wijekoon, Janaka L. [2 ,3 ]
Abeysiriwardhana, W. A. Shanaka P. [4 ]
Wijesundara, Malitha [1 ]
机构
[1] Sri Lanka Inst Informat Technol, Malabe 10115, Sri Lanka
[2] Victorian Inst Technol, Adelaide Campus, Adelaide, SA 5000, Australia
[3] Keio Univ, Dept Syst Design Engn, Yokohama 1080073, Japan
[4] ACSL Ltd, Tokyo 1340086, Japan
来源
IEEE ACCESS | 2025年 / 13卷
关键词
Diseases; YOLO; Accuracy; Crops; Cameras; Plant diseases; Nutrients; Insects; Convolutional neural networks; Predictive models; Coconut; pest control; transfer learning; deep learning; image processing; mask R-CNN; YOLOv5; YOLOv8; YOLO11; ARTIFICIAL NEURAL-NETWORK;
D O I
10.1109/ACCESS.2025.3537664
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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%.
引用
收藏
页码:24463 / 24477
页数:15
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