A Survey on the State of Art Approaches for Disease Detection in Plants
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Abstract
Agriculture is the main factor for economy and contributes to GDP. The growth of the economy of many countries is based on agriculture. As a result, the yield factor, quality and volume of agricultural products, play a critical role in economic development. Plant diseases and pests have become a major determinant of crop yields throughout the years, as such illnesses in plants offer a serious threat and impediment to higher yields or production in the agriculture industry. As a result, From the outset, it becomes the major duty to correctly monitor the plants, to detect diseases thoroughly, and to determine methods of controlling or monitoring these plant diseases pests in order to achieve a higher rate of production growth and minimal crop damage. Using machine vision, deep learning methods and tools for extracting and classifying features, It could be possible to build a reliable disease detection system. Numerous researchers have created and deployed various ways for detecting plant diseases and pests. The potential of these methods has been examined in this work.
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References
Saleem, M.H. ,Potgieter, J., and Arif, K. M. (2021) “ Plant disease detection and classification by deep learning,” Plants, p.468.
Guan, X. (2021) "A Novel Method of Plant Leaf Disease Detection Based on Deep Learning and Convolutional Neural Network," 6th International Conference on Intelligent Computing and Signal Processing (ICSP), pp. 816-819, doi: 10.1109/ICSP51882.2021.9408806.
Barbedo, J.G.A (2019) ” Plant disease identification from individual lesions and spots using deep learning,” Biosystems Engineering, pp.96-107.
Kawasaki, Y., Uga, H., Kagiwada, S., and Iyatomi, H. (2015)” December. Basic study of automated diagnosis of viral plant diseases using convolutional neural networks,” International symposium on visual computing, pp. 638-645.
Brahimi, M. , Arsenovic, M., Laraba, S. , Sladojevic, S., Boukhalfa, K., and Moussaoui, A.(2017) “Deep learning for plant diseases: detection and saliency map visualization,” In Human and machine learning, pp. 93-117
Jasim, M.A., and Al-Tuwaijari, J.M.(2020) ” Plant leaf diseases detection and classification using image processing and deep learning techniques,” International Conference on Computer Science and Software Engineering (CSASE), pp. 259-265.
Nagaraju, M. , and Chawla, P. (2020)” Systematic review of deep learning techniques in plant disease detection,” International Journal of System Assurance Engineering and Management, pp.547-560.
Amara, J. ,Bouaziz, B., and Algergawy, A. (2017)”A deep learning-based approach for banana leaf diseases classification,” Datenbanksystemefür Business, Technologie und Web.
Guo, Y. , Zhang, J., Yin, C., Hu, X., Zou, Y., Xue, Z., and Wang, W. (2020)” Plant disease identification based on deep learning algorithm in smart farming,” Discrete Dynamics in Nature and Society.
Hruška, J., Adão, T., Pádua, L., Marques, P., Cunha, A., Peres, E., Sousa., A., Morais, R., and Sousa, J.J.(2018)“Machine learning classification methods in hyperspectral data processing for agricultural applications, ”International Conference on Geoinformatics and Data Analysis pp. 137-141.
Bai, X., Li, X., Fu, Z. ,Lv, X., and Zhang, L. (2017)” A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images,” Computers and Electronics in Agriculture, pp.157-165,doi: 10.1016/j.compag.2017.03.004.
Barbedo, J.G.A.(2016)”A review on the main challenges in automatic plant disease identification based on visible range images,” Biosystems engineering, pp.52-60.
Brahimi, M., Boukhalfa, K., and Moussaoui, A.(2017) ”Deep learning for tomato diseases: classification and symptoms visualization,” Applied Artificial Intelligence , pp.299-315, doi: 10.1080/08839514.2017.1315516.
Cruz, A.C., Luvisi, A., De Bellis, L., and Ampatzidis, Y. (2017) ” X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion,” Frontiers in plant science, pp.1741, doi:10.3389/fpls.2017.01741.
DeChant, C. ,Wiesner-Hanks, T. , Chen, S., Stewart, E.L., Yosinski, J. , Gore, M.A., Nelson, R.J., and Lipson, H.(2017)” Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning,” Phytopathology, pp.1426-1432.
Durmu?, H. ,Güne?, E.O., and K?rc?, M.(2017)” August. Disease detection on the leaves of the tomato plants by using deep learning,” 6th International Conference on Agro-Geoinformatics, pp. 1-5.
Ferentinos, K.P. (2018)” Deep learning models for plant disease detection and diagnosis,” Computers and electronics in agriculture, pp.311-318.
Fuentes, A.., Yoon, S., Kim, S.C, and Park, D.S. (2017) “A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition,” Sensors, pp.2022.
Mindhe, O. ,Kurkute, O. , Naxikar, S. , and Raje, N. (2020) “ Plant disease detection using deep learning,” International Research Journal of Engineering and Technology, pp. 2497-2503.
Lu, Y. , Yi, S., Zeng, N., Liu, Y. , and Zhang, Y.(2017)” Identification of rice diseases using deep convolutional neural networks,” Neurocomputing, pp.378-384.
Ferreira, A. dos Santos, Freitas, D.M., Silva, G.G. da, Pistori, H. , and Folhes, M.T.(2017) “ Weed detection in soybean crops using ConvNets,” Computers and Electronics in Agriculture, pp.314-324.
Oppenheim, D., and Shani, G.(2017)” Potato disease classification using convolution neural network,” Advances in Animal Biosciences, pp.244-249.
Arshad, M. S., Rehman, U. A., and Fraz, M. M. (2021) "Plant Disease Identification Using Transfer Learning," 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), pp. 1-5, doi: 10.1109/ICoDT252288.2021.9441512.
Chellapandi, B. ,Vijayalakshmi, M. , and Chopra, S. (2021) "Comparison of Pre-Trained Models Using Transfer Learning for Detecting Plant Disease," 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 383-387, doi: 10.1109/ICCCIS51004.2021.9397098.
Srinidhi, V. V. ,Sahay, A., and Deeba, K.(2021) "Plant Pathology Disease Detection in Apple Leaves Using Deep Convolutional Neural Networks : Apple Leaves Disease Detection using EfficientNet and DenseNet," 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1119-1127, doi: 10.1109/ICCMC51019.2021.9418268.
KP.,A., and Anitha, J.(2021) "Plant disease classification using deep learning," 2021 3rd International Conference on Signal Processing and Communication (ICPSC), pp. 407-411, doi: 10.1109/ICSPC51351.2021.9451696.
Ashok, S., Kishore, G., Rajesh, V. ,Suchitra, S., Sophia, S. G. G., and Pavithra, B.(2020) "Tomato Leaf Disease Detection Using Deep Learning Techniques," 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 979-983, doi: 10.1109/ICCES48766.2020.9137986.
Militante, S. V. , Gerardo, B. D., and Medina, R. P.(2019) "Sugarcane Disease Recognition using Deep Learning," 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)” , pp. 575-578, doi: 10.1109/ECICE47484.2019.8942690.
Sladojevic, S. ,Arsenovic, M. ,Anderla, A., Culibrk, D., and Stefanovic, D.(2016)” Deep neural networks based recognition of plant diseases by leaf image classification,” Computational intelligence and neuroscience.
Vaishnnave, M.P. , Devi, K.S., Srinivasan, P., and Jothi, G.A.P.(2019)” Detection and classification of groundnut leaf diseases using KNN classifier,” IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1-5.
Khitthuk, C., Srikaew, A. ,Attakitmongcol, K. , and Kumsawat, P.(2018)” Plant leaf disease diagnosis from color imagery using co-occurrence matrix and artificial intelligence system,” International Electrical Engineering Congress (iEECON),pp. 1-4.
Jhuria, M. , Kumar, A., and Borse, R. ,Anand, R. ,Veni, S. , and Aravinth, J.(2013)” An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method,” International conference on recent trends in information technology (ICRTIT) , pp. 1-6.
SK Hasane Ahammad, , D. K. J.Saini, Phishing URL detection using machine learning methods, Advances in Engineering Software,Volume 173, 2022,103288, ISSN 0965-9978, https://doi.org/10.1016/j.advengsoft.2022.103288
Yadav, P, S. Kumar, and D. K. J.Saini. “A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 8, Aug. 2022, pp. 110-5, doi:10.17762/ijritcc.v10i8.5683.
Jang Bahadur, D. K. , and L.Lakshmanan. “Enhancement of Quality of Service Based on Cross-Layer Approaches in Wireless Sensor Networks”. Journal of Theoretical and Applied Information Technology ISSN: 1992-8645, 15th October 2022. Vol.100. No 19 ,
Jang Bahadur, D. K. , and L.Lakshmanan. “Virtual Infrastructure Based Routing Algorithm for IoT Enabled Wireless Sensor Networks With Mobile Gateway”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 8, Aug. 2022, pp. 96-103, doi:10.17762/ijritcc.v10i8.5681.
Shailesh Kamble, Dilip Kumar J. Saini, Vinay Kumar, Arun Kumar Gautam, Shikha Verma, Ashish Tiwari & Dinesh Goyal (2022) Detection and tracking of moving cloud services from video using saliency map model, Journal of Discrete Mathematical Sciences and Cryptography, 25:4, 1083-1092, DOI: 10.1080/09720529.2022.2072436
Jang Bahadur, D. K. , and L.Lakshmanan “Improve Quality of Service in Optimization of Job Scheduling using a Hybrid Approach” Xi'an Shiyou Daxue Xuebao (Ziran Kexue Ban)/ Journal of Xi'an Shiyou University, Natural Sciences Edition ISSN:1673-064X Vol: 65 Issue 01 | 2022 DOI 10.17605/OSF.IO/DW57E
Jang Bahadur, D. K. , and L.Lakshmanan “Wireless Sensor Network Optimization for Multi-Sensor Analytics in Smart Healthcare System” Specialusis Ugdymas, ISSN NO: 1392-5369 Vol. 1 No. 43 (2022)
Saini D., An IoT and fog computing enabled intelligent health care monitoring system to the cloud storage, Turkish Journal of Computer and Mathematics Education (TURCOMAT) Vol12 No10(2021), 2085-2091( ISSN: 1309-4653)
Saini D, Novel Approach of Data Transformation for Privacy Assurance using Optimization of Genetic Algorithm, Design Engineering (Toronto) ISSN No:0011-9342 Scopus Indexed
D. K. Jang Bahadur Saini, P. Patil, K. Dev Gupta, S. Kumar, P. Singh and M. Diwakar, "Optimized Web Searching Using Inverted Indexing Technique," 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 2022, pp. 351-356, doi: 10.1109/CSNT54456.2022.9787680.
D. Siddharth, D. K. J Saini, P. Singh, An Efficient Approach for Edge Detection Technique using Kalman Filter with Artificial Neural Network, International Journal of Engineering, Transactions C: Aspects Vol. 34, No. 12, (2021) 2604-2610
Anand, R. ,Veni, S. , and Aravinth, J.(2016)” An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method,” International conference on recent trends in information technology (ICRTIT) , pp. 1-6.
Elhassouny, A. , and Smarandache, F.(2019)” Smart mobile application to recognize tomato leaf diseases using Convolutional Neural Networks,” International Conference of Computer Science and Renewable Energies (ICCSRE),pp. 1-4.
Gaikwad, V.P. , and Musande, V.(2017)” Wheat disease detection using image processing,” 1st international conference on intelligent systems and information management (ICISIM) pp. 110-112.
Islam, M. ,Dinh, A., Wahid, K., and Bhowmik, P.(2017)” Detection of potato diseases using image segmentation and multiclass support vector machine,” IEEE 30th canadian conference on electrical and computer engineering (CCECE), pp. 1-4.
Pawar, R. , and Jadhav,A. (2017) ” Pomogranite disease detection and classification,” IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 2475-2479.