Deep Learning-Based Surveillance System for Coconut Disease and Pest Infestation Identification

被引：3

作者：

Vidhanaarachchi, S. P. ^{[1
]}

Akalanka, P. K. G. C. ^{[1
]}

Gunasekara, R. P. T., I ^{[1
]}

Rajapaksha, H. M. U. D. ^{[1
]}

Aratchige, N. S. ^{[2
]}

Lunugalage, Dilani ^{[1
]}

Wijekoon, Janaka L. ^{[1
]}

机构：

[1] Sri Lanka Inst Informat Technol, Fac Comp, New Kandy Rd, Malabe, Sri Lanka

[2] Coconut Res Inst, Crop Protect Div, Lunuwila, Sri Lanka

来源：

2021 IEEE REGION 10 CONFERENCE (TENCON 2021) | 2021年

关键词：

Coconut diseases; Pest control; CNN; Mask R-CNN; YOLOV5; Disease dispersion; Crowdsourcing; Image processing;

D O I：

10.1109/TENCON54134.2021.9707404

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The coconut industry which contributes 0.8% to the national GDP is severely affected by diseases and pests. Weligama coconut leaf wilt disease and coconut caterpillar infestation are the most devastating; hence early detection is essential to facilitate control measures. Management strategies must reach approximately 1.1 million coconut growers with a wide range of demographics. This paper reports a smart solution that assists the stakeholders by detecting and classifying the disease, infestation, and deficiency for the sustainable development of the coconut industry. It leads to the early detections and makes stakeholders aware about the dispersions to take necessary control measures to save the coconut lands from the devastation. The results obtained from the proposed method for the identifications of disease, pest, deficiency, and degree of diseased conditions are in the range of 88% - 97% based on the performance evaluations.

引用

页码：405 / 410

页数：6

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