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Bhardwaj, Anshul
- Convolutional neural network architecture for detection and classification of diseases in fruits
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1 Amity University, Noida 201 313, IN
1 Amity University, Noida 201 313, IN
Source
Current Science, Vol 122, No 11 (2022), Pagination: 1315-1320Abstract
Artificial intelligence is now becoming a part of people’s everyday lives. It can help farmers detect any disease in the early stage and take pre-emptive actions to save their crops and control disease spread, thus preventing crop wastage as well as increasing their income. The present study uses a combination of 13 convolutional neural network (CNN) models to classify five types of fruits and their leaf images into 41 classes, including diseased and healthy. Results show that the average accuracy of this CNN architecture is above 90% for all 13 individual models. One of the CNN models has been compared with three pre-trained models, i.e. MobileNet, DenseNet121 and InceptionV3 trained using the same dataset. It shows that the CNN architecture used in this study has higher accuracy while also being simple and easy to train.Keywords
Agriculture, Artificial Intelligence, Convolutional Neural Network, Deep Learning, Fruit and Leaf Disease DetectionFull Text
References
- Gustavsson, J., Food and Agriculture Organization of the United Nations and ASME/Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, N., Global food losses and food waste: extent, causes and prevention: study conducted for the International Congress ‘Save Food!’ at Interpack, Düsseldorf, Germany, 2011.
- https://planthealthaction.org/news/plant-health-facts (accessed on 18 December 2021).
- James, G. M. and Sujatha, S., Categorising apple fruit diseases employing hybrid neural clustering classifier. Mater. Today: Proc., 2021.
- Sembiring, A., Away, Y., Arnia, F. and Muharar, R., Development of concise convolutional neural network for tomato plant disease classification based on leaf images. J. Phys.: Conf. Ser., IOP Publishing Ltd, 2021.
- Xiao, J. R., Chung, P. C., Wu, H. Y., Phan, Q. H., Yeh, J. L. A. and Hou, M. T. K., Detection of strawberry diseases using a convolutional neural network. Plants, 2021, 10, 1–14.
- Ashwinkumar, S., Rajagopal, S., Manimaran, V. and Jegajothi, B., Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks. Mater. Today: Proc., Elsevier Ltd, 2021, pp. 480–487.
- Zia ur Rehman, Khan, M. A., Ahmed, F., Damaševičius, R., Naqvi, S. R., Nisar, W. and Javed, K., Recognizing apple leaf diseases using a novel parallel real-time processing framework based on MASK RCNN and transfer learning: an application for smart agriculture. IET Image Process., 2021, 15, 2157–2168.
- Jogekar, R. N. and Tiwari, N., A review of deep learning techniques for identification and diagnosis of plant leaf disease. Smart Innov., Syst. Technol., 2021, 182, 435–441.
- Wan Nurazwin Syazwani, R., Muhammad Asraf, H., Megat Syahirul Amin, M. A. and Nur Dalila, K. A., Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning. Alexandria Eng. J., 2022, 61, 1265–1276.
- Verma, S., Chug, A. and Singh, A. P., Application of convolutional neural networks for evaluation of disease severity in tomato plant. J. Discrete Math. Sci. Cryptogra., 2020, 23, 273–282.
- Alim, M. M. F., Subiyanto and Sartini, Identification of diseases in tomato leaves using convolutional neural network and transfer learning method. J. Phys.: Conf. Ser., 2021, 1918.
- Ilic, M., Ilic, S., Jovic, S. and Panic, S., Early cherry fruit pathogen disease detection based on data mining prediction. Comput. Electron. Agric., 2018, 150, 418–425.
- Anton, A., Rustad, S., Shidik, G. F. and Syukur, A., Classification of tomato plant diseases through leaf using gray-level co-occurrence matrix and color moment with convolutional neural network methods. Smart Innov., Syst. Technol., 2021, 182, 291–299.
- Ashok, V. and Vinod, D. S., A novel fusion of deep learning and android application for real-time mango fruits disease detection. Adv. Intell. Syst. Comput., 2021, 1171, 781–791.
- Brahimi, M., Boukhalfa, K. and Moussaoui, A., Deep learning for tomato diseases: classification and symptoms visualization. Appl. Artif. Intell., 2017, 31, 299–315.
- Abadi, M. et al., TensorFlow: large-scale machine learning on heterogeneous distributed systems, 2016.
- Chollet, F. and others, Keras, 2015; https://github.com/fchollet/keras
- Buitinck, L. et al., API design for machine learning software: experiences from the Scikit-learn project, 2013.
- Mckinney, W., Data structures for statistical computing in Python, 2010.
- Harris, C. R. et al., Array programming with NumPy. Nature, 2020, 585, 357–362.
- Hunter, J. D., Matplotlib: a 2D graphics environment. Comput. Sci. Eng., 2007, 9, 90–95.
- Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T. and Andreetto, M., MobileNets: efficient convolutional neural networks for mobile vision applications, 2017.
- Huang, G., Liu, Z., van der Maaten, L. and Weinberger, K. Q., Densely connected convolutional networks, 2017.
- Szegedy, C., Vanhoucke, V., Ioffe, S. and Shlens, J., Rethinking the inception architecture for computer vision, 2015.
- Facilitating Learner Centric Decision Making for Massive Open Online Courses
Abstract Views :98 |
PDF Views:1
Authors
Source
Journal of Engineering Education Transformations, Vol 35, No 3 (2022), Pagination: 123-132Abstract
Learners opt for MOOCs as it provides multiple benefits and the most important being that these courses can be accessed anytime and from anywhere with internet access. Learners can study their desired course at their convenience of time and desired pace. But as there are multiple options for them, they may get confused about selecting a course. This study highlights the findings of implementing the Analytical Hierarchical Process (AHP), a Multiple Criteria Decision Making (MCDM) technique used to choose the best option in case of multiple alternatives. Attributes were selected from literature and a survey was administered to learners. Responses were analyzed and courses were ranked based on their scores. From the analysis, we interpret that usefulness of the course in the University Curriculum is the most preferred criteria while selecting the course.Keywords
MOOC, Multiple Criteria Decision Making, Analytical Hierarchical Process.
References
- Alexander, M. (2012). Decision-Making using the Analytic Hierarchy Process (AHP). International Journal of Scientific Research, 3(6), 135–136. https://doi.org/10.15373/22778179/june2014/47
- Chen, D., Feng, Y., Zhao, Z., Jiang, J., & Yu, J. (2014). Does MOOC really work effectively. Proceedings of the 2014 IEEE International Conference on MOOCs, Innovation and Technology in Education, IEEE MITE 2014, 272–277. https://doi.org/10.1109/MITE.2014.7020287
- Chosy Yuda Sakti, Kelly Rossa Sungkono, & Riyanarto Sarno. (2019). Determination of Hospital Rank by Using Analytic Optimization on the Basis of Ratio Analysis. 178–183.
- Gandhi, S., Mangla, S. K., Kumar, P., & Kumar, D. (2016). A combined approach using AHP and DEMATEL for evaluating success factors in implementation of green supply chain management in Indian manufacturing industries. International Journal of Logistics Research and Applications, 19(6), 537–561. https://doi.org/10.1080/13675567.2016.1164126
- Gené, O. B., Núñez, M. M., & Blanco, Á. F. (2014). Gamification in MOOC. 215–220. https://doi.org/10.1145/2669711.2669902
- Harrold, J. W., & Sandland, J. (2018). The Influence of Immediate Homework Feedback on Student Performance and Satisfaction in an Engineering MOOC. Proceedings of 2018 Learning With MOOCS, LWMOOCS 2018, September, 90–93. https://doi.org/10.1109/LWMOOCS.2018.8534 592
- Kuruoglu, E., Guldal, D., Mevsim, V., & Gunvar, T. (2015). Which family physician should I choose? The analytic hierarchy process approach for ranking of criteria in the selection of a family physician. BMC Medical Informatics and Decision Making, 15(1), 1–8. https://doi.org/10.1186/s12911-015-0183-1
- Lucy Bodenham. (2019). The advantages of learning via MOOCs. University of London. https://london.ac.uk/news-opinion/london-connection/feature/advantages-learning-moocs
- Obeidat, M. S., Qasim, T., & Khanfar, A. (2018). Implementing the AHP multi-criteria decision approach in buying an apartment in Jordan. Journal of Property Research, 35(1), 53–71https://doi.org/10.1080/09599916.2017.1413588
- Saaty, T. L. (1984). The Analytic Hierarchy Process: Decision Making in Complex Environments. Quantitative Assessment in Arms Control, 285–308. https://doi.org/10.1007/9781-4613-2805-6_12
- Sachdeva, A., Singh, P. K., & Sharma, A. (2016). MOOCs: A comprehensive study to highlight its strengths and weaknesses. Proceedings of the 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education, MITE 2015, 365–370. https://doi.org/10.1109/MITE.2015.7375346
- Shrivastava, A., Hasteer, N., & Soni, K. M. (2019). Perspectives on Curriculum Inclusive MOOCs for Engineering Education: Reflection & Learning. Proceedings - IEEE 10th International Conference on Technology for Education, T4E 2019, 34–37. https://doi.org/10.1109/T4E.2019.00015
- Shrivastava, A., Hasteer, N., & Soni, K. M. (2021). Implications of the Intertwined Effects of Curriculum Inclusive MOOCs. 11(6). https://doi.org/10.18178/ijiet.2021.11.6.1525
- Singh, P. K., Nand, P., & Gandhi, I. (2014). MOOCS : The Paradigm-shift in Indian Education. 317–320.