Leaf Disease Detection and Prevention Using Machine Learning
DOI:
https://doi.org/10.18486/ijcsnt/10.1.129Keywords:
Leaf Disease Detection, Machine Learning, Image Processing, Canny Edge Detection, K-Means Clustering, Remote SensingAbstract
Plant illnesses are often brought on by pests, insects, and pathogens, and if they are not promptly handled, they significantly reduce yield. Farmers are losing money as a result of different crop diseases. When the cultivated area is enormous, measured in acres, the cultivators find it tiresome to routinely check on the crops. The suggested approach offers a way to automatically diagnose diseases using photos from remote sensing while also offering a solution for routinely monitoring the agricultural area. The suggested approach alerts the farmer about crop illnesses so they may take additional action. The suggested technology aims to identify infections early, as soon as they begin to spread to the leaf’s outer layer. The two phases of the proposed system’s operation start with training data sets. This involves using training sets with both healthy and sick data. The second stage involves crop monitoring and disease identification using Canny’s edge detection technology.
References
S. S. Sannakki and V. S. Rajpurohit, “Classification of Pomegranate Diseases Based on Back Propagation Neural Network,” International Research Journal of Engineering and Technology (IRJET), vol. 2, no. 2, May 2015.
P. R. Rothe and R. V. Kshirsagar, “Cotton Leaf Disease Identification using Pattern Recognition Techniques,” International Conference on Pervasive Computing (ICPC), 2015. DOI: https://doi.org/10.1109/PERVASIVE.2015.7086983
Aakanksha Rastogi, Ritika Arora and Shanu Sharma, “Leaf Disease Detection and Grading using Computer Vision Technology & Fuzzy Logic,” 2nd International Conference on Signal Processing and Integrated Networks (SPIN), 2015. DOI: https://doi.org/10.1109/SPIN.2015.7095350
Godliver Owomugisha, John A. Quinn, Ernest Mwebaze and James Lwasa, “Automated Vision-Based Diagnosis of Banana Bacterial Wilt Disease and Black Sigatoka Disease,” Proceedings of the 1st International Conference on the Use of Mobile ICT in Africa, 2014.
Uan Tian, Chunjiang Zhao, Shenglian Lu and Xinyu Guo, “SVM-Based Multiple Classifier System for Recognition of Wheat Leaf Diseases,” Proceedings of the 2010 Conference on Dependable Computing (CDC 2010), November 20–22, 2010.
S. Yun, W. Xianfeng, Z. Shanwen and Z. Chuanlei, “PNN-Based Crop Disease Recognition with Leaf Image Features and Meteorological Data,” International Journal of Agricultural and Biological Engineering, vol. 8, no. 4, p. 60, 2015.
J. G. A. Barbedo, “Digital Image Processing Techniques for Detecting, Quantifying and Classifying Plant Diseases,” SpringerPlus, vol. 2, no. 660, pp. 1–12, 2013. DOI: https://doi.org/10.1186/2193-1801-2-660
A. Caglayan, O. Guclu and A. B. Can, “A Plant Recognition Approach Using Shape and Color Features in Leaf Images,” in International Conference on Image Analysis and Processing, pp. 161–170, Springer, Berlin, Heidelberg, 2013. DOI: https://doi.org/10.1007/978-3-642-41184-7_17
X. Zhen, Z. Wang, A. Islam, I. Chan and S. Li, “Direct Estimation of Cardiac Bi-Ventricular Volumes with Regression Forests,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014, 2014. DOI: https://doi.org/10.1007/978-3-319-10470-6_73
P. Wang, K. Chen, L. Yao, B. Hu, X. Wu, J. Zhang et al., “Multimodal Classification of Mild Cognitive Impairment Based on Partial Least Squares,” 2016. DOI: https://doi.org/10.3233/JAD-160102
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Rajshekar Gaithonde, Dayanand Jamkhandi, Padmanjali A Hagargi, Guru Prasad
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
