Citation

Bhuiyan, Md Roman and Islam, Md Baharul and Abdullah, Junaidi and Badie, Farshad and Napolitano, Giulio (2025) Automatic Insect Pest Identification and Recognition for Paddy Crops Pest Control. In: 14th International Conference on Image Processing, Theory, Tools and Applications, IPTA 2025, 13 October 2025 - 16 October 2025, Istanbul.

Text Automatic Insect Pest Identification and Recognition for Paddy Crops Pest Control.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Agriculture is one of the main economic pursuits of the 64 Bangladeshi districts.: in Bangladesh, about seventy percent of the workforce rely on agriculture for their living. Bangladesh’s gross national revenue is much enhanced by the country’s rice farming but attacks of insect pests have a great impact on rice harvests. Different insect pests require different management measures, so the accurate identification of paddy field insects is a crucial task that allows, e.g., the application of the appropriate poison for specific insect pests. This will also prevent the wasteful use of ineffective insecticides. The main challenge addressed in this work is to detect and instantly segment small harmful insects in paddy fields. To address this problem, we have used deep convolutional neural network (DCNN) learning, based on Mask-RCNN, therefore enabling a technique for visual localisation and classification of agricultural pest insects. We have also developed our own dataset of harmful insect annotated images. In the proposed Mask-RCNN model we used a ResNet101 backbone, which can detect and segment at the same time. The proposed model achieves an AP@0.5 of 85.7%, a mAP of 63.8%, and an AR@10 of 68.5, therefore generating an anticipated accuracy of 75%. ResNet101 performs better on all measures. The suggested approach should be able to identify and classify the small harmful insects, with suitable accuracy, in real-world deployments.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Agriculture, Mask-RCNN, insect detection, instant segmentation, DCNN
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines S Agriculture > S Agriculture (General)
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	06 Jan 2026 07:19
Last Modified:	07 Jan 2026 03:50
URII:	http://shdl.mmu.edu.my/id/eprint/15133

Downloads

Edit (login required)