REFERENCES
- Ai, Y., Sun, C., Tie, J., Cai, X. 2020. Research on recognition model of crop diseases and insect pests based on deep learning in harsh environments. IEEE Access, 8, 171686–171693.
- Andrianto, H., Faizal, A., Armandika, F. 2020. smartphone application for deep learning-based rice plant disease detection. In 2020 International Conference on Information Technology Systems and Innovation (ICITSI), 387–392.
- Badrinarayanan, V., Kendall, A., Cipolla, R. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481–2495.
- Burhan, S.A., Minhas, S., Tariq, A., Hassan, M.N. 2020. Comparative study of deep learning algorithms for disease and pest detection in rice crops. In 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 1–5.
- Chiranjeevi. M., Velmathi. G. 2022. Phototactic behavior of yellow stemborer and rice leaf folder moths to surface mount device-light emitting diodes of various wavelengths. International Journal of Applied Science and Engineering, 19, 1–9.
- Durmuş, H., Güneş, E.O., Kırcı, M., 2017. Disease detection on the leaves of the tomato plants by using deep learning. In 2017 6th International Conference on Agro-Geoinformatics, 1–5.
- Figueroa, M., Hammond‐Kosack, K.E., Solomon, P.S. 2018. A review of wheat diseases–A field perspective. Molecular Plant Pathology, 19, 1523–1536.
- Girshick, R. 2015. Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, 1440–1448.
- Hao, L., Wan, F., Ma, N., Wang, Y. 2018. Analysis of the development of WeChat mini program. Journal of Physics: Conference Series, 1087, 062040.
- Karim, S., Tong, G., Li, J., Qadir, A., Farooq, U., Yu, Y. 2023. Current advances and future perspectives of image fusion: A comprehensive review. Information Fusion, 90, 185–217.
- Karim, S., Zhang, Y., Asif, M.R., Ali, S. 2017. Comparative analysis of feature extraction methods in satellite imagery. Journal of Applied Remote Sensing, 11.
- Khakimov, A., Salakhutdinov, I., Omolikov, A., Utaganov, S. 2022. Traditional and current-prospective methods of agricultural plant diseases detection: A review. In IOP Conference Series: Earth and Environmental Science, 951, 012002.
- Khan, A.I., Quadri, S.M.K., Banday, S., Shah, J.L. 2022. Deep diagnosis: A real-time apple leaf disease detection system based on deep learning. Computers and Electronics in Agriculture, 198, 107093.
- Laghari, A.A., He, H., Shafiq, M., Khan, A. 2018. Assessment of quality of experience (QoE) of image compression in social cloud computing. Multiagent and Grid Systems, 14, 125–143.
- Lu, J., Hu, J., Zhao, G., Mei, F., Zhang, C. 2017. An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture, 142, 369–379.
- Nagasubramanian G., Sakthivel R.K., Patan R, Sankayya M., Daneshmand M., Gandomi H. 2021. Ensemble classification and IoT-based pattern recognition for crop disease monitoring system. IEEE Internet of Things Journal, 8, 12847–12854.
- Samson. A.A., Popoola. O., Mbey. V. 2022. An alternative framework for implementing generator coherency prediction and islanding detection scheme considering critical contingency in an interconnected power grid. International Journal of Applied Science and Engineering, 19, 1–12.
- Shah, J.P., Prajapati, H.B., Dabhi, V.K. 2016. A survey on detection and classification of rice plant diseases. In 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), 1–8.
- Shruthi, U., Nagaveni, V., Raghavendra, B.K. 2019. A review on machine learning classification techniques for plant disease detection. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 281–284.
- Simonyan, K., Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
- Singh, A., SV, H.J., Aishwarya, D., Jayasree, J.S. 2022. Plant disease detection and diagnosis using deep learning. In 2022 International Conference for Advancement in Technology (ICONAT), 1–6.
- Subramanian, M., Shanmugavadivel, K., Nandhini, P.S. 2022. On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Computing and Applications, 1–18.
- Vallabhajosyula, S., Sistla, V., Kolli, V.K.K. 2022. Transfer learning-based deep ensemble neural network for plant leaf disease detection. Journal of Plant Diseases and Protection, 129, 545–558.
- Wang, X., Yin, S., Liu, D., Li. H., Karim, S. 2020. Accurate playground localisation based on multi-feature extraction and cascade classifier in optical remote sensing images. International Journal of Image and Data Fusion, 11, 233–250.
- Yin, S., Li, H., Liu, D., Karim, S. 2020. Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation. Multimedia Tools and Applications, 79, 31049–31068.
- Yin, S., Li, H. 2020. Hot region selection based on selective search and modified fuzzy C-means in remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5862–5871.
- Yujia, L. 2017. System implementation and prospect analysis of wechat “small program” development [J]. Information Communication, 1, 1–2.
- Zhong, Y., Zhao, M. 2020. Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture, 168, 105146.