Insect classification and detection in field crops using modern machine learning techniques
Tài liệu tham khảo
Shen, 2018, Detection of stored-grain insects using deep learning, Comput Electron Agric, 145, 319, 10.1016/j.compag.2017.11.039
Bhargava, 2018, Fruits and vegetables quality evaluation using computer vision: A review, J King Saud Univ Comput Inf Sci
Yaakob, 2012, An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant, Appl Intell, 37, 12, 10.1007/s10489-011-0310-3
Yue, 2018, Deep recursive super resolution network with Laplacian Pyramid for better agricultural pest surveillance and detection, Comput Electron Agric, 150, 26, 10.1016/j.compag.2018.04.004
Cheng, 2017, Pest identification via deep residual learning in complex background, Comput Electron Agric, 141, 351, 10.1016/j.compag.2017.08.005
Xie, 2018, Multi-level learning features for automatic classification of field crop pests, Comput Electron Agric, 152, 233, 10.1016/j.compag.2018.07.014
Nanni L, Maguolo G, Pancino F. Research on insect pest image detection and recognition based on bio-inspired methods; 2019. arXiv: 1910.00296.
Deng, 2020, Application of agricultural insect pest detection and control map based on image processing analysis, J Intell Fuzzy Syst, 38, 379, 10.3233/JIFS-179413
Garcia, 2017, A distributed-means segmentation algorithm applied to lobesia botrana recognition, Complexity, 14
Bakkay, 2017, Automatic detection of individual and touching moths from trap images by combining contour-based and region-based segmentation, IET Comput Vis, 12, 138, 10.1049/iet-cvi.2017.0086
Wang, 2012, A new automatic identification system of insect images at the order level, Knowl-Based Syst, 33, 102, 10.1016/j.knosys.2012.03.014
Xie, 2015, Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning, Comput Electron Agric, 119, 123, 10.1016/j.compag.2015.10.015
Yang, 2015, A tool for developing an automatic insect identification system based on wing outlines, Sci Rep, 5, 1
Coates, 2011, Selecting receptive fields in deep networks, Adv Neural Inf Process Syst, 2528
Lucas, 2019, Proximity Forest: an effective and scalable distance-based classifier for time series, Data Min Knowl Disc, 33, 607, 10.1007/s10618-019-00617-3
Liu, 2020, Image classification algorithm based on deep learning-kernel function, Sci Program, 2020
Nanni L, Brahnam S, Ghidoni S, Maguolo G. General purpose (GenP) bioimage ensemble of handcrafted and learned features with data augmentation; 2019. arXiv: 1904.08084.
Makandar, 2015, Image enhancement techniques using highpass and lowpass filters, Int J Comput Appl, 109, 12
Hambal, 2017, Image noise reduction and filtering techniques, Int J Sci Res, 6, 2033
Wen, 2012, Image-based orchard insect automated identification and classification method, Comput Electron Agric, 89, 110, 10.1016/j.compag.2012.08.008
Mikołajczyk A, Grochowski M. Data augmentation for improving deep learning in image classification problem. In: Proc IIPhDW’ 18 proceedings of the 2018 international interdisciplinary PhD workshop. Swinoujscie, Poland; 2018. p. 117–22.
Shorten, 2019, A survey on image data augmentation for deep learning, J Big Data, 6, 60, 10.1186/s40537-019-0197-0
Thenmozhi K, Reddy US. Image processing techniques for insect shape detection in field crops. In: IEEE 2017 international conference on inventive computing and informatics. Coimbatore, India; 2017; p. 699–704.
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, Comput Electron Agric, 127, 495, 10.1016/j.compag.2016.07.008
Asefpour Vakilian, 2013, Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks, Arch Phytopathol Pflanzenschutz, 46, 1262, 10.1080/03235408.2013.763620
Fuchida, 2017, Vision-based perception and classification of mosquitoes using support vector machine, Appl Sci., 7, 51, 10.3390/app7010051
Favret, 2016, Machine vision automated species identification scaled towards production levels, Syst Entomol, 41, 133, 10.1111/syen.12146
Li, 2018, Automatic identification of butterfly species based on HoMSC and GLCMoIB, Vis Comput, 34, 1525, 10.1007/s00371-017-1426-1
Antony, 2018, A Bayesian classification approach for predicting Gesonia gemma Swinhoe population on soybean crop in relation to abiotic factors based on economic threshold level, JBC, 32, 68, 10.18311/jbc/2018/16309
Martineau, 2017, A survey on image-based insect classification, Pattern Recogn, 65, 273, 10.1016/j.patcog.2016.12.020
Zhu, 2017, Hybrid deep learning for automated lepidopteran insect image classification, Orient Insects, 51, 79, 10.1080/00305316.2016.1252805
Martins VA, Freitas LC, de Aguiar MS, de Brisolara LB, Ferreira PR. Deep learning applied to the identification of fruit fly in intelligent traps. In: Proc SBESC 19 IX Brazilian symposium on computing systems engineering. Rio Grande do Norte, Brazil. IEEE; 2019. p. 1–8.
Sharma, 2019, Performance analysis of deep learning CNN models for disease detection in plants using image segmentation, Inf Process Agric
Deng, 2018, Research on insect pest image detection and recognition based on bio-inspired methods, Biosyst Eng, 169, 139, 10.1016/j.biosystemseng.2018.02.008
A large-scale benchmark dataset for insect pest recognition; 2019. Link: https://github.com/xpwu95/IP102.
Rother, 2004, “GrabCut” interactive foreground extraction using iterated graph cuts, ACM T Graphic, 23, 309, 10.1145/1015706.1015720
Mantovani RG, Rossi AL, Vanschoren J, Bischl B, De Carvalho AC. Effectiveness of random search in SVM hyper-parameter tuning. In: Proc IJCNN’ 15 international joint conference on neural networks, Killarney, Ireland; 2015. p. 1–8.
Cheng X, Zhang YH, Wu YZ, Yue Y. Agricultural pests tracking and identification in video surveillance based on deep learning. In: Proc Part III ICIC ’17 international conference on intelligent computing. Liverpool, UK; 2017. p. 58–70.
Nanni, 2020, Insect pest image detection and recognition based on bio-inspired methods, Ecol Inform., 101089
Abdullah, 2016, Robust iris segmentation method based on a new active contour force with a noncircular normalization. IEEE transactions on systems, man, and cybernetics, Systems, 47, 3128