@ARTICLE{Mewada_Hiren_IoT_2021,
 author={Mewada, Hiren and Patoliaya, Jignesh},
 volume={vol. 67},
 number={No 3},
 journal={International Journal of Electronics and Telecommunications},
 pages={517-522},
 howpublished={online},
 year={2021},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={Leaf - a significant part of the plant, produces food using the process called photosynthesis. Leaf disease can cause damage to the entire plant and eventually lowers crop production. Machine learning algorithm for classifying five types of diseases, such as Alternaria leaf diseases, Bacterial Blight, Gray Mildew, Leaf Curl and Myrothecium leaf diseases, is proposed in the proposed study. The classification of diseases needs front face of leafs. This paper proposes an automated image acquisition process using a USB camera interfaced with Raspberry PI SoC. The image is transmitted to host PC for classification of diseases using online web server. Pre-processing of the acquired image by host PC to obtain full leaf, and later classification model based on SVM is used to detect type diseases. Results were checked with a 97% accuracy for the collection of acquired images.},
 type={Article},
 title={IoT based Automated Plant Disease Classification using Support Vector Machine},
 URL={http://ochroma.man.poznan.pl/Content/121579/PDF-MASTER/72_2746_Mewada_skl.pdf},
 keywords={Plant Disease classification, Support vector machine (SVM), Graph Cut, Gray-level Co-occurance Matrix},
}