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Authors Name
Tissera, BDS [1]
Tissera, HRMF [1]
TL [1]
TMJA2, Cooray [1]
TML [1]
Tomato is highly grown vegetable all over the world. Tomato is highly susceptible to diseases and considerable amount of crop is wasted due to diseases caused by virus, bacteria and fungi. Disease identification of Tomato is a major problem faced by farmers. The proposed system helps farmers to identify four tomato diseases namely Anthracnose, Blossoms End Rot, Late Blight and Powdery Mildew. Convolutional Neural Network (CNN) has been applied in the study to predict the disease from the images. The implementation of CNN from scratch demands high computational resources and considerable amount of image data. Therefore, transfer learning approach has been applied with the MobileNet model which is trained on the ImageNet classification dataset. This research work was conducted by changing the number of images, training models and hyperparameters to experiment the accuracy of the system. The system gained 99.16% of training accuracy, 98.89% of validation accuracy and 98.96% of test accuracy with 0.0001 learning rate, 0.9 momentum, batch size as 32 and 3200 training images. [1]
Toner, MA [1]
Toseef, Umar [1]
TS [2]
Tudugala, R [8]
Turale, S [1]
Turyanska, L [1]
UAK [1]
Ubayachandra, EG [1]
Udagama, Preethi V [1]
Udagearachchi, U.A.H.H. [1]
Udagedara NP [2]
Udani, HLI [1]
Udara, HMM [1]
Udara, RT [1]

Now showing items 5234-5253 of 6038

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