POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea

References

• Amin, M., & Melkamu, F. (2014). Management of Ascochyta blight (Ascochyta rabiei) in Chickpea Using a New Fungicide. Research in Plant Sciences, 2(1), 1–10.
   Google Scholar
• Belay, A. J., Salau, A. O., Ashagrie, M., & Haile, M. B. (2022). Development of a chickpea disease detection and classification model using deep learning. Informatics in Medicine Unlocked, 31, 100970. https://doi.org/10.1016/j.imu.2022.100970
   Google Scholar
• Benzohra, I. E., Bendahmane, B. S., Labdi, M., & Benkada, M. Y. (2012). Determination of pathotypes and physiological races in Ascochyta rabiei, the agent of Ascochyta blight in chickpea (Cicer arietinum L.) in Algeria. African Journal of Agricultural Research, 7(7), 1214–1219. https://doi.org/10.5897/AJAR11.1810
   Google Scholar
• Caballo, C., Castro, P., Gil, J., Millan, T., Rubio, J., & Die, J. V. (2019). Candidate genes expression profiling during wilting in chickpea caused by Fusarium oxysporum f. Sp. Ciceris race 5. PloS One, 14(10), e0224212. https://doi.org/10.1371/journal.pone.0224212
   PubMed Web of Science ®Google Scholar
• Chauhan, R., Ghanshala, K. K., & Joshi, R. (2018). Convolutional Neural Network (CNN) for Image Detection and Recognition [Paper presentation].2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). https://www.academia.edu/44667910/Convolutional_Neural_Network_CNN_for_Image_Detection_and_Recognition https://doi.org/10.1109/ICSCCC.2018.8703316
   Google Scholar
• CSA. (2019). Agricultural sample survey, Area and production of major Crops, private holdings for 2011/12 mehere season, vol I, pp. 1–54.
   Google Scholar
• Dangmei, G., Singh, V. K., & Sharma, A. (2023). Effect of organic source of nutrients on growth, yield and quality of chickpea Effect of organic source of nutrients on growth, yield and quality of chickpea (Cicer arietinum L.). April 2023.
   Google Scholar
• FAO. (2020). World food and agriculture - statistical yearbook, 1–366. Rome. https://doi.org/10.4060/cb1329en
   Google Scholar
• FAO. (2021). World food and agriculture - statistical yearbook 2021, 1–368.  Rome. https://doi.org/10.4060/cb4477en
   Google Scholar
• FAO. (2022). World food and agriculture – statistical yearbook, 1–382. Rome. https://doi.org/10.4060/cc2211en
   Google Scholar
• Foresto, E., Carezzano, M. E., Giordano, W., & Bogino, P. (2023). Ascochyta blight in Chickpea: An update. Journal of Fungi (Basel, Switzerland), 9(2), 203. https://doi.org/10.3390/jof9020203
   PubMedGoogle Scholar
• Haque, M. A., Marwaha, S., Arora, A., Deb, C. K., Misra, T., Nigam, S., & Hooda, K. S. (2022). A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize. Frontiers in Plant Science, 13, 1077568. https://doi.org/10.3389/fpls.2022.1077568
   PubMed Web of Science ®Google Scholar
• Hayit, T., Endes, A., & Hayit, F. (2023). The severity level classification of Fusarium wilt of chickpea by pre ‑ trained deep learning models. Journal of Plant Pathology, 106(1), 93–105. https://doi.org/10.1007/s42161-023-01520-z
   Web of Science ®Google Scholar
• Iman, M., Arabnia, H. R., & Rasheed, K. (2023). A review of deep transfer learning and recent advancements. Technologies, 11(2), 40. https://doi.org/10.3390/technologies11020040
   Web of Science ®Google Scholar
• Jubair. (2011). An Enhanced Decision Based Adaptive Median Filtering Technique to Remove Salt and Pepper Noise in Digital Images. Proceedings of 14th International Conference on Computer and Information Technology (ICClT 2011), ICClT, 22–24.
   Google Scholar
• Khan, M. A., Khan, M. A., Ahmed, F., Mittal, M., Goyal, L. M., Jude Hemanth, D., & Satapathy, S. C. (2020). Gastrointestinal diseases segmentation and classification based on duo-deep architectures. Pattern Recognition Letters, 131, 193–204. https://doi.org/10.1016/j.patrec.2019.12.024
   Web of Science ®Google Scholar
• Krizhevsky, A., & Ilya Sutskever, G. E. H. (2012). Imagenet classifcation with deep convolutional neural networks. Advances in Neural Information Processing Systems. Handbook of Approximation Algorithms and Metaheuristics, 25, 1–9.
   Google Scholar
• Kumar, S., Bhambri, M. C., Porte, S. S., & Saxena, R. R. (2023). Evaluation of chickpea (Cicer arietinum L.). Cultivars under Organic Production System, 12(9), 603–607.
   Google Scholar
• Kundu, N., Rani, G., Singh, V., Gupta, K., Chandra, S., Vocaturo, E., & Zumpano, E. (2022). Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning. Artificial Intelligence in Agriculture, 6, 276–291. https://doi.org/10.1016/j.aiia.2022.11.002
   Google Scholar
• Lamba, S., Kukreja, V., Rashid, J., Gadekallu, T. R., Kim, J., Baliyan, A., Gupta, D., & Saini, S. (2023). A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases. Front. Plant Sci. 14:123406, 01–18. https://doi.org/10.3389/fpls.2023.1234067
   Google Scholar
• Lecun. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.47. https://doi.org/10.1080/0144929X.2020.1856928
   Web of Science ®Google Scholar
• Mahmood, M. T., Ahmad, M., & Ali, I. (2019). Chickpea blight : former efforts on pathogenicity, resistant germplasm and disease management 2 regional agricultural research institute, Bahawalpur. Pakistan Gomal University Journal of Research, 35(1), 1–10.
   Google Scholar
• Pande, S., Sharma, M., Gaur, P. M., Tripathi, S., Kaur, L., Basandrai, A., Khan, T., Gowda, C. L. L., & Siddique, K. H. M. (2011). Development of screening techniques and identification of new sources of resistance to Ascochyta blight disease of chickpea. Australasian Plant Pathology, 40(2), 149–156. https://doi.org/10.1007/S13313-010-0024-8/TABLES/2
   Web of Science ®Google Scholar
• Pastor, S., Crociara, C., Valetti, L., Peña Malavera, A., Fekete, A., Allende, M. J., & Carreras, J. (2022). Screening of chickpea germplasm for Ascochyta blight resistance under controlled environment. Euphytica, 218, 12, 1–11. https://doi.org/10.1007/s10681-021-02963-0
   Web of Science ®Google Scholar
• Rawal, V., & Navarro, D. K. (2019). The Global Economy of Pulses. Rome, FAO. 1–190. http://www.fao.org/3/i7108en/I7108EN.pdf
   Google Scholar
• Sanderson, M. (2010). Christopher D. Manning, prabhakar raghavan, hinrich schütze, introduction to information retrieval, cambridge university press. Natural Language Engineering, 16(1), 100–103. https://doi.org/10.1017/S1351324909005129
   Web of Science ®Google Scholar
• Shi, T., Liu, Y., Zheng, X., Hu, K., Huang, H., Liu, H., & Huang, H. (2023). Recent advances in plant disease severity assessment using convolutional neural networks. Scientific Reports, 13(1), 2336. https://doi.org/10.1038/s41598-023-29230-7
   PubMed Web of Science ®Google Scholar
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–14.
   Google Scholar
• Singh, A. (2016). Comparative analysis of gaussian filter with wavelet denoising for various noises present in images. Indian Journal of Science and Technology, 9(1), 1–8. https://doi.org/10.17485/ijst/2016/v9i47/106843
   Google Scholar
• Smith, R. A., & Wilson, M. S. (2019). Transfer learning in deep neural networks: A review. Medical Image Analysis, 30, 1–18.
   Google Scholar
• Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision [Paper presentation]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 2818–2826. https://doi.org/10.1109/CVPR.2016.308
   Google Scholar
• Venkatesan, R., & Li, B. (2017). Convolutional neural networks in visual computing : A concise guide (1st ed.). CRC Press. https://doi.org/10.4324/9781315154282
   Google Scholar
• Vinothini, R., Niranjana, G., & Yakub, F. (2023). A novel classification model using optimal long short ‑ term memory for classification of COVID ‑ 19 from CT images. Journal of Digital Imaging, 36(6), 2480–2493. https://doi.org/10.1007/s10278-023-00852-7
   PubMed Web of Science ®Google Scholar
• Williams, C. K. I. (2003). Learning with kernels: Support vector machines, regularization, optimization, and beyond. Journal of the American Statistical Association, 98(462), 489–489. https://doi.org/10.1198/jasa.2003.s269
   Google Scholar
• Worku, C., Adugna, M., Mussa, E. C., & Mengstu, M. (2023). Analysis market outlet choice of smallholder chickpea producers in Northwest part of Ethiopia. Cogent Food & Agriculture, 9(2), 1–12. https://doi.org/10.1080/23311932.2023.2285226
   Web of Science ®Google Scholar
• Zhang, N., Zhang, X., Shang, P., Ma, R., Yuan, X., Li, L., & Bai, T. (2023). Detection of cotton verticillium wilt disease severity based on hyperspectrum and GWO-SVM.
   Google Scholar