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Authors/year | Work detail | Algorithm/technique | Platform | Findings |
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K.P. Ferentinos [8]—2018 | Using an opensource dataset, the deep learning model is prepared. | CNN | Android/Web | Android and web-based apps to identify the disease |
Model is used to detect the diseases | Server-based implementation |
Python language is used | The server set up is costly and need maintenance |
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A. Shah, P. Gupta, and Y.M Ajgar [9]—2018 | Automatic solution for nutrient deficiency identification | OpenCV | Web | Image feature extraction logic |
Data set of deficient and healthy leaves is used | Supervised ML | Serve-based implementation |
RGB colour pattern is extracted | | The server set up is costly and need maintenance |
|
J. Shirahatti, P. Patil, and P. Akulwar [10]—2018 | Studied different kinds of ML techniques | SVM, PNN, ANN, GA | Web | Comparative study with results |
Accuracy result comparison is performed |
Image data set is used for comparison |
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K.K. Singh [11]—2018 | System to track, diagnose and forecast the disease information | CNN | Web | Cloud-based integration |
Local expert advice can be taken | Interface to upload the image and see the result |
Geo tagging on images | High accuracy ∼ 95% |
Image dataset is used | Local expert opinion |
Cloud-based implementation is costly and need maintenance |
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