| Autor: |
Gaikwad, Vijay, Patil, Gaurav N., Patil, Gaurav R., Phad, Chaitanya, Pujari, Himaja, Rathod, Payal |
| Předmět: |
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| Zdroj: |
Grenze International Journal of Engineering & Technology (GIJET); 2023, Vol. 9 Issue 2, p788-796, 9p |
| Abstrakt: |
Smart agriculture and plant disease prediction have become important areas of research due to the increasing demand for food production and the need for sustainable farming practices. The paper provides a review of smart agricultural practises and how machine learning algorithms are used to predict plant diseases. Smart agriculture is based on the use of technology such as the Internet of Things (IoT) which enable farmers to monitor and manage their farms remotely. Additionally, machine learning algorithms can be used to accurately identify plant diseases through analysing the input plant image. Three machine learning algorithms have been used in this paper i.e. SVM, VGG19, and KNN. VGG16 is used for the feature extraction. With the 13 different types of diseases, highest accuracy system achieved is 99%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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