用深度学习提前识别椰子病虫害，准确率超90%。
Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation using Deep Learning-based Image Processing Techniques
- 基于迁移学习的CNN和Mask R-CNN识别病虫害
- 病害识别准确率达90%-97%，虫害数量检测精度超95%
- 适合农业监测与智能植保系统应用
全球椰子种植面临病虫害导致的产量损失问题，尤其在斯里兰卡及周边国家，威利加玛椰子叶枯病（WCWLD）和椰子虫害（CCI）对椰树造成严重损害。当前依赖人工现场观察，效率低且难以早期发现。本文在斯里兰卡马塔拉、普塔拉姆和马坎杜拉地区采集数据，采用基于迁移学习的卷积神经网络（CNN）和掩码区域-候选网络（Mask R-CNN）实现WCWLD和CCI的早期识别与病情评估。同时，使用YOLO目标检测模型统计虫害叶片上的虫数。结果表明，所提方法对WCWLD和CCI的识别准确率分别为90%和95%；病害严重程度分类准确率达97%；不同YOLO模型对虫数计数的准确率为：YOLOv5-96.87%，YOLOv8-96.1%，YOLO11-95.9%。
原文摘要 · Abstract (English)
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%.
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