Abdulridha, J., Ampatzidis, Y., Kakarla, S. C., & Roberts, P. (2020). Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques.
Precision Agriculture,
21(7), 955-978.
https://doi.org/ 10.1007/s11119-019-09703-4.
Asefpour Vakilian, K., & Massah, J. (2017). A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops.
Computers and Electronics in Agriculture,
139(9), 153-163.
https://doi.org/10.1016/j.compag.2017.05.012.
Chen, J., Zheng, H., Lin, X., Wu, Y., & Su, M. (2018). A novel image segmentation method based on fast density clustering algorithm.
In Engineering Applications of Artificial Intelligence,
73(10), 92-110.
https://doi.org/10.1016/j.engappai.2018.04.023.
Jaisakthi, S. M., Mirunalini P., & Thenmozhi, D. (2019). Grape leaf disease identification using machine learning techniques. In Proceedings of the 2019 International Conference on Computational Intelligence in Data Science (ICCIDS). Feb. 23-25, Chennai, India.
Javidan, S. M., Banakar, A., Vakilian, K. A., & Ampatzidis, Y. (2022). A feature selection method using slime mould optimization algorithm in order to diagnose plant leaf diseases. In Proceedings of the 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). Dec. 28-29. Kashan, Iran. (in Persian)
Javidan, S. M., Banakar, A., Vakilian, K. A., & Ampatzidis, Y. (2023). Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning.
Smart Agricultural Technology, 3(3), 100081-100110.
https://doi.org/10.1016/j.atech.2022.100081.
Javidan, S. M., Banakar, A., Vakilian, K. A., Ampatzidis, Y., & Rahnama, K. (2024). Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning
. Multimedia Tools and Applications,
83(5), 20-43.
https://doi.org/10.1007/s11042-024-18214-y.
Kumar, S., Sharma, B., Sharma, V. K., Sharma, H., & Bansal, J. C. (2020). Plant leaf disease identification using exponential spider monkey optimization.
Sustainable Computing: Informatics and Systems,
28(3), 100283-100294.
https://doi.org/10.1016/j.suscom.2018.10.004.
Makhadmeh, S. N., Al-Betar, M. A., Abasi, A. K., Awadallah, M. A., Doush, I. A., Alyasseri, Z. A. A., & Alomari, O. A. (2022). Recent advances in butterfly optimization algorithm, its versions and applications. Archives of Computational Methods in Engineering, 30(2), 1399-1420. https://link.springer.com/article/10.1007/s11831-022-09843-3.
Mohammadzamani, D., Sajadian, S., & Javidan, S. M. (2020). Detection of
Callosobruchus maculatus F. with image processing and artificial neural network.
Applied Entomology and Phytopathology,
88(1), 103-112.
https://doi.org/10.22092/jaep.2020.341684.1324. (in Persian)
Mohammadzamani, D., Javidan, S. M., Zand, M., & Rasouli, M. (2023). Detection of Cucumber Fruit on Plant Image Using Artificial Neural Network.
Journal of Agricultural Machinery,
13(1). 18-29.
https://doi.org/10.22067/jam.2022.73827.1077.
Padol, P. B., & Yadav, A. A. (2016). SVM classifier based grape leaf disease detection. In Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), April 12-15. Lisbon, Portugal.
Roostaei, P., Rasouli, M., & Babaei, A. (2015). Study of compatibility and the effect of pollen of some grape cultivars on fruit set and quantitative and qualitative characters of fruit, cv. Rish Baba Sefid. Plant Production Technology, 7(1), 193-210. (in Persian)
Sadeghian, Z., Akbari, E., & Nematzadeh, H. (2021). A hybrid feature selection method based on information theory and binary butterfly optimization algorithm.
Engineering Applications of Artificial Intelligence,
97(5), 104079-104084.
https://doi.org/10.1016/j.engappai.2020.104079.
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review.
Journal of Biomedical Informatics, 85(3), 189–203.
https://arxiv.org/abs/1711.08421.
Xie, X., Ma, Y., Liu, B., He, J., Li, S., & Wang, H. (2020). A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks.
Frontiers in Plant Science, 11(2), 751-766.
https://doi.org/10.3389/fpls.2020.00751.
)