Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN

Siti Khairunniza-Bejo, Mohd Firdaus Ibrahim (), Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad and Mohammad Aufa Mhd Bookeri
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Siti Khairunniza-Bejo: Institute of Plantation Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
Mohd Firdaus Ibrahim: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Marsyita Hanafi: Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Mahirah Jahari: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Fathinul Syahir Ahmad Saad: Department of Mechatronic, Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Arau 02600, Malaysia
Mohammad Aufa Mhd Bookeri: Engineering Research Centre, Malaysian Agriculture Research and Development Institute, Seberang Perai 13200, Malaysia

Agriculture, 2024, vol. 14, issue 9, 1-16

Abstract: Counting planthoppers manually is laborious and yields inconsistent results, particularly when dealing with species with similar features, such as the brown planthopper ( Nilaparvata lugens ; BPH), whitebacked planthopper ( Sogatella furcifera ; WBPH), zigzag leafhopper ( Maiestas dorsalis ; ZIGZAG), and green leafhopper ( Nephotettix malayanus and Nephotettix virescens ; GLH). Most of the available automated counting methods are limited to populations of a small density and often do not consider those with a high density, which require more complex solutions due to overlapping objects. Therefore, this research presents a comprehensive assessment of an object detection algorithm specifically developed to precisely detect and quantify planthoppers. It utilises annotated datasets obtained from sticky light traps, comprising 1654 images across four distinct classes of planthoppers and one class of benign insects. The datasets were subjected to data augmentation and utilised to train four convolutional object detection models based on transfer learning. The results indicated that Faster R-CNN VGG 16 outperformed other models, achieving a mean average precision (mAP) score of 97.69% and exhibiting exceptional accuracy in classifying all planthopper categories. The correctness of the model was verified by entomologists, who confirmed a classification and counting accuracy rate of 98.84%. Nevertheless, the model fails to recognise certain samples because of the high density of the population and the significant overlap among them. This research effectively resolved the issue of low- to medium-density samples by achieving very precise and rapid detection and counting.

Keywords: insect pest detection; deep learning; machine vision; VGG16 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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