Dibyanarayan Hazra
ABSTRACT
Each cuisine required tomato in their kitchen for various food items and this makes tomato most popular crop worldwide and India is in second rank in terms production of tomato. Now a days, production of tomato goes down because of various diseases and to treat these diseases farmer needs to have extensive prior knowledge about the pathogen and along with various factor which promote the disease in the tomato. Due to lack of knowledge, the disease spreads rapidly and destroys all crops. To fill this gap, deep learning (DL) has been playing an important role, and there is much research on DL, how it can be used in medical industry and the agriculture industry for the identification of disease using images. There is a limitation for DL model that it does not work well with small dataset and huge amount of samples are required to train the model. Moreover, the data are not shared openly for security or for any other reason. Therefore, to overcome this challenge a Federated Learning (FL) based approach has been presented in the article. In FL, a deep learning model is shared with organizations which having the data and train the model. After training, the model information is shared with a centralized server which designs a generalized model. After getting the generalized model, it is shared with all other sites. The process is repeated until a generalized model is not designed and well-suited with all the sites. In our study, we tested our model on a tomato leaf disease data set using FL methodology with 10 clients and achieved the best precision with 88. 01%.
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Index Terms
- Generalized framework using Federated Learning for tomato disease classification over unbalanced dataset
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