Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring

Biosystems Engineering - Tập 176 - Trang 140-150 - 2018
Yu Sun1, Xuanxin Liu1, Mingshuai Yuan1, Lili Ren2, Jianxin Wang1, Zhibo Chen1
1School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
2School of Forestry, Beijing Forestry University, Beijing 100083, China

Tài liệu tham khảo

Carde, 1995, Control of moth pests by mating disruption: Successes and constraints, Annual Review of Entomology, 40, 559, 10.1146/annurev.en.40.010195.003015 Chollet, 2017 Dai, 2016, R-FCN: Object detection via region-based fully convolutional networks, Neural Information Processing Systems, 379 Deng, 2018, Research on insect pest image detection and recognition based on bio-inspired methods, Biosystems Engineering, 169, 139, 10.1016/j.biosystemseng.2018.02.008 Ding, 2016, Automatic moth detection from trap images for pest management, Computers and Electronics in Agriculture, 123, 17, 10.1016/j.compag.2016.02.003 Ebrahimi, 2017, Vision-based pest detection based on SVM classification method, Computers and Electronics in Agriculture, 137, 52, 10.1016/j.compag.2017.03.016 Espinoza, 2016, 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, Computers and Electronics in Agriculture, 127, 495, 10.1016/j.compag.2016.07.008 Everingham, 2015, The pascal visual object classes challenge: A retrospective, International Journal of Computer Vision, 111, 98, 10.1007/s11263-014-0733-5 Fuentes, 2017, A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition, Sensors, 17, 2022, 10.3390/s17092022 Garcia, 2017, A distributed K-means segmentation algorithm applied to lobesia botrana recognition, Complexity, 14 He, 2017 He, 2016, Deep residual learning for image recognition, Computer Vision and Pattern Recognition, 770 Hough, 1962 Howard, 2017 Keras. from https://keras.io/. Kingma, 2014 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Lin, 2017 Lin, 2014 Liu, 2016 Li, 2015, Detection of small-sized insect pest in greenhouses based on multifractal analysis, Optik - International Journal for Light and Electron Optics, 126, 2138, 10.1016/j.ijleo.2015.05.096 Maharlooei, 2017, Detection of soybean aphids in a greenhouse using an image processing technique, Computers and Electronics in Agriculture, 132, 63, 10.1016/j.compag.2016.11.019 Redmon, 2015 Ren, 2015, Faster R-CNN: Towards real-time object detection with region proposal networks TensorFlow. from: https://www.tensorflow.org/. Witzgall, 2010, Sex pheromones and their impact on pest management, Journal of Chemical Ecology, 36, 80, 10.1007/s10886-009-9737-y Xia, 2015, Automatic identification and counting of small size pests in greenhouse conditions with low computational cost, Ecological Informatics, 29, 139, 10.1016/j.ecoinf.2014.09.006 Yalcin, 2015 Yao, 2013, Segmentation of touching insects based on optical flow and NCuts, Biosystems Engineering, 114, 67, 10.1016/j.biosystemseng.2012.11.008