Abstract
Artificial Intelligence is occupying an extensive position in every field of study. In the field of Food and Agriculture it is an emerging topic which is gaining lot of attention. Such techniques are much smarter compared to traditional techniques. So it has successfully emerged into this domain of study. Inspired by its potentiality, this work is an attempt to apply deep transfer learning which is a sub category of artificial intelligence. Here transfer learning is used to classify tomatoes into their maturity classes. Three approaches of transfer learning viz. VGG, Inception and ResNet are implemented in this work. Tomato dataset is used during the experiment which consisted of all the three classes of tomatoes. The models were evaluated by training them iteratively with varying epoch number and batch size. From the results it is seen that VGG 19 performed best at epoch 50 and batch size 32. The other models also showed good results proving transfer learning to be a viable solution in solving food related problems.
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