Logo PTI Logo rice

Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

Root Rot Lentil and Healthy Lentil Detection Using Image Processing

, , , ,

DOI: http://dx.doi.org/10.15439/2022R08

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 227234 ()

Full text

Abstract. The hardest thing to do in agriculture is to figure out which leaves are healthy and which ones are damaged. Bangladesh makes 80\\% of its money from farming. Most farmers cannot read or write. They didn't know how much fertilizer to put on a lentil with root rot or a healthy lentil. They sometimes spray medicine on the plants, which is terrible for them. As a result, agriculture has become much less productive. In this paper, a picture-segmenting algorithm is given that can automatically find and classify plant leaf diseases. Also included are surveys of different ways to classify diseases that can be used to find plant leaf diseases. The Convolution Neural Network model is used to segment images, an essential part of finding plant leaf diseases. Every country's growth is based on its agricultural production. To keep agricultural production at a certain level and keep growing sustainably, scientists need to study how to find and treat diseases. Standard methods in the literature for classifying leaf images involve extracting attributes and training classifier models, which makes them less accurate. The technique suggested gets rid of any unnecessary data from the image collection. Using the mixture model for region growth, we first find the area of interest based on the colors of the leaves in the image. After extracting the features, a deep convolution neural network model is used to classify the leaf photos. A convolutional neural network model can be used with the deep learning model to find different patterns in color photos. Examining the execution strategy of the proposed model using an unauthorized dataset. According to the results of the simulating replica, the suggested model outperforms the well-known current methods in the field, with mean classification accuracy and area under the characteristics curve of 95.35\\% and 94.7\\%, respectively.

References

  1. Dhaygude, S. B. and Kumbhar, N. P., Agricultural plant leaf disease detection using image processing. Int J Adv Res Electr Electron Instrum 3: 2(1), 2013
  2. S. Arivazhagan, R. Newlin Shebiah, S. Ananthi, S. Vishnu Varthini, Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int: CIGR Journal. 15(1): 211-217, 2013
  3. Kulkarni Anand H, Ashwin Patil RK. Applying image processing technique to detect plant diseases. Int J Mod EngRes. 2(5):3661–4, 2012
  4. Savita N. Ghaiwat, Parul Arora, Detection and Classification of Plant Leaf Diseases Using Image processing Techniques: A Review, IJRAET, Vol 2, Issue 3, 2014
  5. Sabah Bashir, Navdeep Sharma, Remote Area Plant Disease Detection Using Image Processing, IOSR Journal of Electronics and Communication Engineering, Volume 2, Issue 6, PP 31-34, 2012
  6. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM, Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics, and Transfer Learning. IEEE Trans Med Imaging. 35(5):1285-98, 2016
  7. B. J. Thompson, E. Dougherty, Mathematical Morphology in Image Processing (1st ed.), 1993
  8. B. Bhanu and Jing Peng, Adaptive integrated image segmentation and object recognition, in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews),427-441,2000
  9. QT. Hoang, N.C. Minh, DT. Tran, Designing a Remote Monitoring System for Lakes Based on Internet of Things. In: DT. Tran, G. Jeon, T.D.L. Nguyen, J. Lu, TD. Xuan, Intelligent Systems and Networks. ICISN; vol 243, 2021
  10. A. S. M. A. Akib, S. Mahmud and M. F. Mridha, Future Micro Hydro Power: Generation of Hydroelectricity and IoT based Monitoring System, International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2021, pp. 298-302
  11. M. R. Badnakhe and P. R. Deshmukh, An Application of K-Means Clustering and Artificial Intelligence in Pattern Recognition for Crop Diseases”, International Conference on Advancements in Information Technology, vol.20 (2011)
  12. Smita Naikwadi, Niket Amoda, Advances In Image Processing For Detection Of Plant Diseases, International Journal Of Application Or Innovation In Engineering and Management, 2(11), 2013
  13. B. Sanjay, Leaf disease severity measurement using image processing,in International Journal of Engineering and Technology, 3(5), 297-301, 2011
  14. P. Chaudhary, A. K. Chaudhari, A.N. Cheeran, Sharda Godara, Color Transeform Based Approach for Disease spot Detection on Plant Leaf,IJCST, 3(6), 2012
  15. T. Quang-Huy, V. T. Duong, L. Q. Hai and T. Duc-Tan, Image Reconstruction Utilizing Algebraic Helmoltz Inversion and Passband Filtering Applied to Viscoelasticity, International Conference on Multimedia Analysis and Pattern Recognition (MAPR), 1-5, 2020
  16. N. C. Minh, T. H. Dao, D. N. Tran, N. Q. Huy, N. T. Thu and D. T. Tran, Evaluation of Smartphone and Smartwatch Accelerometer Data in Activity Classification, 8th NAFOSTED Conference on Information and Computer Science (NICS), 33-38, 2021
  17. A. Devaraj, K. Rathan, S. Jaahnavi and K. Indira, Identification of Plant Disease using Image Processing Technique, International Conference on Communication and Signal Processing (ICCSP), 0749-0753, 2019
  18. N. Tasnia, S. Mahmud and M. F. Mridha, COVID-19 Future Forecasting Tool: Infected Patients Recovery and Hospitalization Trends Using Deep Learning Models, International Conference on Science and Contemporary Technologies (ICSCT), 1-6, 2021