Machine Learning (ML) is the subfield in Artificial Intelligence (AI) that works dynamically to solve several issues. ML mainly focused on understanding the structure of the data and selecting the specific model based on the given dataset. Nowadays plant diseases are becoming very dangerous to farmers. Various plant diseases are identified by many researchers based on the pathogen. Several visible and invisible features are present to identify plant diseases. Visible features such as shape, size, silting are most widely used to analyze the condition of the plant. In this paper, the adaptive clustering algorithm (ACA) is introduced to detect diseases in plants. To show the disease-affected region the fuzzy c-means (FCM) clustering approach is adopted to highlight the disease-affected region with red patches which are called clusters. To improve the performance of the proposed approach the feature subset selection is used to increase the effectiveness and scalability. The output results show the performance of the ACA.

Machine learning (ML), ACA, AI and K-Means

[1] F. Fina, P. Birch, R. Young, J. Obu, B. Faithpraise, and C. Chatwin, "Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters," Int. J. Adv. Biotechnol. Res., vol. 4,no. 2, pp. 189–199, Jul. 2013.
[2] M. A. Ebrahimi, M. H. Khoshtaghaza, S. Minaei, and B. Jamshidi, "Vision-based pest detection based on SVM classification method," Comput. Electron. Agricult., vol. 137, pp. 52–58, May 2017.
[3] S. R. Dubey and A. S. Jalal, "Adapted approach for fruit disease identification using images," Int. J. Comput. Vis. Image Process., vol. 2, no. 3, pp. 44–58, Jul. 2012.
[4] L. Li, S. Zhang and B. Wang, "Plant Disease Detection and Classification by Deep Learning—A Review," in IEEE Access, vol. 9, pp. 56683-56698, 2021, doi: 10.1109/ACCESS.2021.3069646.
[5] X. Liu, W. Min, S. Mei, L. Wang and S. Jiang, "Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach," in IEEE Transactions on Image Processing, vol. 30, pp. 2003-2015, 2021, doi: 10.1109/TIP.2021.3049334.
[6] Jayme G.A. Barbedo, Factors influencing the use of deep learning for plant disease recognition, Biosystems Engineering, Volume 172, 2018, https://doi.org/10.1016/j.biosystemseng.2018.05.013.
[7] P. Shah, H. B. Prajapati and V. K. Dabhi, "A survey on detection and classification of rice plant diseases," 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), pp. 1-8, 2016. doi: 10.1109/ICCTAC.2016.7567333.
[8] L. Shanmugam, A. L. A. Adline, N. Aishwarya and G. Krithika, "Disease detection in crops using remote sensing images," 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), pp. 112-115, 2017. doi: 10.1109/TIAR.2017.8273696.
[9] Q. Wu, Y. Chen, and J. Meng, "DCGAN-based data augmentation for tomato leaf disease identification," IEEE Access, vol. 8, pp. 98716–98728, 2020.
[10] M. A. H. Abas, N. Ismail, A. I. M. Yassin and M. N. Taib, "VGG16 for plant image classification with transfer learning and data augmentation", Int. J. Eng. Technol., vol. 7, pp. 90-94, Oct. 2018.
[11] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, "Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks," IEEE Access, vol. 7, pp. 59069–59080, 2019.
[12] B. Wang and D. Wang, "Plant leaves classification: A few-shot learning method based on Siamese network," IEEE Access, vol. 7, pp. 151754–151763, 2019.
[13] W. Yang, C. Yang, Z. Hao, C. Xie, and M. Li, "Diagnosis of plant cold damage based on hyperspectral imaging and convolutional neural network," IEEE Access, vol. 7, pp. 118239–118248, 2019.
[14] tansal, P., Yadav, H. and Sunkaria, R.K., “Impulse noise removal usinm MDBUTMF gith histomram estimation”, In Advance Computing Conference (IACC), International on, pp. 468-471, IEEE, 2015.
[15] Ramakrishnan M., Sahaya Anselin Nisha, “Groundnut leaf disease detection and classification by usinm back probamation almorithm”, In Communications and Signal Processing (ICCSP), International Conference on, pp. 0964-0968, IEEE, 2015.