Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse

[1]  Kyunghyun Cho,et al.  Augmentation for small object detection , 2019, 9th International Conference on Advances in Computing and Information Technology (ACITY 2019).

[2]  Jing Zhang,et al.  Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting , 2020, Comput. Electron. Agric..

[3]  Yiquan Wu,et al.  Recent advances in small object detection based on deep learning: A review , 2020, Image Vis. Comput..

[4]  Fuchun Sun,et al.  HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Irene Vänninen,et al.  Yellow sticky traps for decision-making in whitefly management : What has been achieved? , 2013 .

[6]  Chengjun Xie,et al.  AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection , 2020, Comput. Electron. Agric..

[7]  Hyeonjoon Moon,et al.  Crop pest recognition in natural scenes using convolutional neural networks , 2020, Comput. Electron. Agric..

[8]  Haiyang Zhou,et al.  A smart-vision algorithm for counting whiteflies and thrips on sticky traps using two-dimensional Fourier transform spectrum , 2017 .

[9]  A. Maki,et al.  Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks , 2019, Systematic biology.

[10]  Yu Jian,et al.  A Crop Pests Image Classification Algorithm Based on Deep Convolutional Neural Network , 2017 .

[11]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[12]  Jian Dong,et al.  Attentive Contexts for Object Detection , 2016, IEEE Transactions on Multimedia.

[13]  Luc Van Gool,et al.  Efficient Non-Maximum Suppression , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[14]  H. Aliakbarpour,et al.  Evaluation of Yellow Sticky Traps for Monitoring the Population of Thrips (Thysanoptera) in a Mango Orchard , 2011, Environmental entomology.

[15]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[16]  Yangyang Li,et al.  Anchor-Free Single Stage Detector in Remote Sensing Images Based on Multiscale Dense Path Aggregation Feature Pyramid Network , 2020, IEEE Access.

[17]  Graham W. Taylor,et al.  Automatic moth detection from trap images for pest management , 2016, Comput. Electron. Agric..

[18]  Tae-Soo Chon,et al.  Automatic identification and counting of small size pests in greenhouse conditions with low computational cost , 2015, Ecol. Informatics.

[19]  U. Srinivasulu Reddy,et al.  Crop pest classification based on deep convolutional neural network and transfer learning , 2019, Comput. Electron. Agric..

[20]  Tae-Soo Chon,et al.  Density estimation of Bemisia tabaci (Hemiptera: Aleyrodidae) in a greenhouse using sticky traps in conjunction with an image processing system , 2008 .

[21]  Daniel J. Bearup,et al.  Estimating insect population density from trap counts , 2012 .

[22]  Alejandro López,et al.  Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture , 2016, Comput. Electron. Agric..

[23]  Dongjian He,et al.  FLYOLOv3 deep learning for key parts of dairy cow body detection , 2019, Comput. Electron. Agric..

[24]  Tae-Soo Chon,et al.  In situ detection of small-size insect pests sampled on traps using multifractal analysis , 2012 .

[25]  Rodrigo Castañeda-Miranda,et al.  Original paper: Scale invariant feature approach for insect monitoring , 2011 .

[26]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[27]  Yufeng Shen,et al.  Detection of stored-grain insects using deep learning , 2018, Comput. Electron. Agric..

[28]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Hanqing Lu,et al.  Attention CoupleNet: Fully Convolutional Attention Coupling Network for Object Detection , 2019, IEEE Transactions on Image Processing.