Quarternion color image processing as an alternative to classical grayscale conversion approaches for pest detection using yellow sticky traps

Rodríguez, Luis Alberto Rodríguez; Castañeda-Miranda, Celina Lizeth; Lució, Mireya Moreno; Solís-Sánchez, Luis Octavio; Castañeda‐Miranda, Rodrigo

doi:10.1016/j.matcom.2020.11.022

Cited by 7 publications

(1 citation statement)

References 8 publications

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“…Much research on automatic pest detection has been proposed to improve the efficiency of pest detection. At present, automated pest detection methods can be divided into two main categories: sensor-based methods [ 2 , 3 , 4 , 5 ] and visual image-based methods [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion

Zha

Qian

et al. 2021

Entropy

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Traditional pest detection methods are challenging to use in complex forestry environments due to their low accuracy and speed. To address this issue, this paper proposes the YOLOv4_MF model. The YOLOv4_MF model utilizes MobileNetv2 as the feature extraction block and replaces the traditional convolution with depth-wise separated convolution to reduce the model parameters. In addition, the coordinate attention mechanism was embedded in MobileNetv2 to enhance feature information. A symmetric structure consisting of a three-layer spatial pyramid pool is presented, and an improved feature fusion structure was designed to fuse the target information. For the loss function, focal loss was used instead of cross-entropy loss to enhance the network’s learning of small targets. The experimental results showed that the YOLOv4_MF model has 4.24% higher mAP, 4.37% higher precision, and 6.68% higher recall than the YOLOv4 model. The size of the proposed model was reduced to 1/6 of that of YOLOv4. Moreover, the proposed algorithm achieved 38.62% mAP with respect to some state-of-the-art algorithms on the COCO dataset.

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Section: Introductionmentioning

confidence: 99%

A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion

Zha

Qian

et al. 2021

Entropy

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Rapid density estimation of tiny pests from sticky traps using Qpest RCNN in conjunction with UWB-UAV-based IoT framework

Juan,

Ke,

Chen

et al. 2023

Neural Comput & Applic

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Custom CornerNet: a drone-based improved deep learning technique for large-scale multiclass pest localization and classification

Albattah

Masood

Javed

et al. 2022

Complex Intell. Syst.

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Insect pests are among the most critical factors affecting crops and result in a severe reduction in food yield. At the same time, early and accurate identification of insect pests can assist farmers in taking timely preventative steps to reduce financial losses and improve food quality. However, the manual inspection process is a daunting and time-consuming task due to visual similarity between various insect species. Moreover, sometimes it is difficult to find an experienced professional for the consultation. To deal with the problems of manual inspection, we have presented an automated framework for the identification and categorization of insect pests using deep learning. We proposed a lightweight drone-based approach, namely a custom CornerNet approach with DenseNet-100 as a base network. The introduced framework comprises three phases. The region of interest is initially acquired by developing sample annotations later used for model training. A custom CornerNet is proposed in the next phase by employing the DenseNet-100 for deep keypoints computation. The one-stage detector CornerNet identifies and categorizes several insect pests in the final step. The DenseNet network improves the capacity of feature representation by connecting the feature maps from all of its preceding layers and assists the CornerNet model in detecting insect pests as paired vital points. We assessed the performance of the proposed model on the standard IP102 benchmark dataset for pest recognition which is challenging in terms of pest size, color, orientation, category, chrominance, and lighting variations. Both qualitative and quantitative experimental results showed the effectiveness of our approach for identifying target insects in the field with improved accuracy and recall rates.

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Quarternion color image processing as an alternative to classical grayscale conversion approaches for pest detection using yellow sticky traps

Cited by 7 publications

References 8 publications

A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion

A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion

Rapid density estimation of tiny pests from sticky traps using Qpest RCNN in conjunction with UWB-UAV-based IoT framework

Custom CornerNet: a drone-based improved deep learning technique for large-scale multiclass pest localization and classification

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