A Survey of Existing Leaf Disease Techniques using Artificial Neural Network
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : RakshitKhajuria, Dr.Sunanda, Mr.Siddharth Singh|
|DOI : 10.14445/22312803/IJCTT-V59P109|
RakshitKhajuria, Dr.Sunanda, Mr.Siddharth Singh "A Survey of Existing Leaf Disease Techniques using Artificial Neural Network". International Journal of Computer Trends and Technology (IJCTT) V59(1):52-62, May 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Agriculture productivity is something on which Indian economy heavily depends. Major portion of India depends on agriculture. But due to diseases in leaves there is a great loss to farmers. In earlier times disease detection in plants was carried out by naked eyes observation method but it is not very efficient at medium to large scale. Automatic detection techniques can be used for disease detection in plants is efficient and time saving and accuracy. The automation of plant disease identification has gained attention in last few years. So, with the improvement in ANN, its families and Machine learning techniques there is a significant scope of improvement in the pre-existing methodologies for leaf disease detection, segmentation and identification. With the help of modern sensors and imaging techniques the efficiency and accuracy of ANN model have significantly improved, as we know that the process is highly dependent on qualityof data sets and the algorithm we use to process these datasets. This study focuses on various implementations of these ANN’s and their benefits such that they deal out optimal or near optimal solutions.
 Anne-KatrinMahlein et al,"Recent advances in sensing plant diseases for precision crop protection", Eur J Plant Pathology, vol.133, pp. 197-209, 2012, DOI 10.1007/s10658-011-9878-z.
 Anicet K. Kouakou et al," Cucumber mosaic virus detection by artificial neural network using multispectral and multimodal imagery", J.T.Zoueu, vol.133, pp. 11250-11257, 2016, DOI http://dx.doi.org/10.1016/j.ijleo.2016.09.035 0030-4026.
 TrimiNehaTete and SushmaKamlu,? Detection of Plant Disease Using Threshold, K-MeanCluster and ANN Algorithm", International Conference for Convergence in Technology, vol.133, pp. 523-526, 2017, DOI 978-1-5090-4307-1.
 Kuo-Yi Huang," Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features", Computers and Electronics in Agriculture, vol.57, pp. 3-11, 2007, DOI 10.1016/j.compag.2007.01.015
 Xin Yang andTingweiGuo," Machine learning in plant disease research", European Journal of Bio Medical Research, vol.133, pp.6-9, 2017, DOI http://dx.doi.org/10.18088/ejbmr.3.1.2016
 Pranjali B. Padol and S. D. Sawant," Fusion Classification Technique Used to DetectDowny and Powdery Mildew Grape Leaf Diseases", International Conference on Global Trends in Signal Processing, pp. 298-301, 2016, DOI 978-1-5090-0467-6
 Alessandro dos Santos Ferreira et al," Weed detection in soybean crops using ConvNets", Computers and Electronics in Agriculture, vol.143, pp. 314-324, 2017, DOI https://doi.org/10.1016/j.compag.2017.10.027
 Jiang Lu, Jie Hu et al," An in-field automatic wheat disease diagnosis system", Computers and Electronics in Agriculture, vol.142, pp. 369-379, 2017, DOI https://doi.org/10.1016/j.compag.2017.09.012
 Konstantinos P. Ferentinos,? Deep learning models for plant disease detection and diagnosis", Computers and Electronics in Agriculture, vol.145, pp. 311-318, 2018, DOI https://doi.org/10.1016/j.compag.2018.01.009
 Yang Lu et al," Identification of rice diseases using deep convolutional neural networks", National Natural Science Foundation of China, vol.267, pp. 378-384, 2017, DOI http://dx.doi.org/10.1016/j.neucom.2017.06.023
 Xinjie Yu," Deep-learning-network and hyperspectral imaging for oilseed leaf". Chemometrics and Intelligent Laboratory Systems, PII S0169-7439(17)30678-0, 2017 DOI 10.1016/j.chemolab.2017.12.010
 SrdjanSladojevic, Marko Arsenovic et al." Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification", HINDAWI, Article ID 3289801, 2016, DOI http://dx.doi.org/10.1155/2016/3289801
 Amanda Ramcharan et al," DL method for Image-Based Cassava Disease Detection", FRONTIERS, vol.8, Article 1852, 2017, DOI 10.3389/fpls.2017.01852
 Sue Han Lee et al," DEEP-PLANT: PLANT IDENTIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS", 2015, DOI 1506.08425v1
 Jiang Lu et al," An In-field Automatic Wheat Disease Diagnosis System", Information Science and Technology, 2017, DOI 1710.08299v1
 PornntiwaPawara et al,? Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition", 6th International Conference on Pattern Recognition Applications and Methods, SBN: 978-989-758-222-6, pp. 479-486, 2017, DOI 10.5220/0006196204790486
 KamilDimililer and EhsanKiani," Application of back propagation neural networks on maize plant detection", 9th International Conference on Theory and Application of Soft Computing, vol.120, pp. 376-381, 2017, DOI 10.1016/j.procs.2017.11.253
 Xiaoli Wang et al," Automatic Detection of Rice Disease Using Near Infrared SpectraTechnologies", J Indian Soc Remote Sens, vol. 45(5), pp. 785-794, 2017, DOI 10.1007/s12524-016-0638-6
 Dheeb Al Bashish, Malik Barik et al," Detection and Classification of leaf diseases using K-means based segmentation and Neural Network based Classification", Information Techonology,vol.10(2), pp. 267-275, 2011, DOI 10.3923/itj.2011.267.275
 Ramakrishnan. M and Sahaya Anselin Nisha. A," Groundnut Leaf Disease Detection and Classification by using BackProbagation Algorithm", ICCSP 2015 conference, pp. 0964-0968, 2015, DOI 978-1-4 799-8081-9
 DanijelaVukadinovicet al," Automated Detection of Mycosphaerella Melonis Infected Cucumber Fruits", International Federation of Automatic Control, vol.49-16, pp. 105-109, 2016, DOI 10.1016/j.ifacol.2016.10.020
 JingweiHou et al," Detection of grapevine leaf roll disease based on 11-index imagery and ant colony clustering algorithm", Cross Mark, vol.17, pp. 488-505,2016, DOI 10.1007/s11119-016-9432-2
 Na Wu et al," A LDA-based segmentation model for classifying pixels in crop diseased images", National Natural Science Foundation,ISSN: 1934-1768, 2017, DOI 10.23919/ChiCC.2017.8029194
 Uwe Knauer et al," Improved classification accuracyof powdery mildew infection levels of wine grapes by spatial spectral analysis of hyper spectral images", Cross Mark, 2017,DOI 10.1186/s13007-017-0198-y
 Patrick Wspanialy et al," Early powdery mildew detection system for application in greenhouse automation", Computers and Electronics in Agriculture, vol.127, pp. 487-494, 2016, DOI http://dx.doi.org/10.1016/j.compag.2016.06.027
Artificial neural network, Plant pathology, Principle Component Analysis (PCA), Support Vector Machines