Performance evaluation of ResNet model for classification of tomato plant disease
Abstract Objectives The plant tomato (Solanum Lycopersicum) is vastly infected by various
diseases. Exact diagnosis on time contributes a significant job to the good production of
tomato crops. The key objective of this article is to recognize the infection in tomato leaves
with better accuracy and in less time. Methods Nowadays deep convolutional neural
networks have attained surprising outcomes in several applications, together with the
categorization of tomato leaves infected with several diseases. Our work is based on deep …
diseases. Exact diagnosis on time contributes a significant job to the good production of
tomato crops. The key objective of this article is to recognize the infection in tomato leaves
with better accuracy and in less time. Methods Nowadays deep convolutional neural
networks have attained surprising outcomes in several applications, together with the
categorization of tomato leaves infected with several diseases. Our work is based on deep …
Objectives
The plant tomato (Solanum Lycopersicum) is vastly infected by various diseases. Exact diagnosis on time contributes a significant job to the good production of tomato crops. The key objective of this article is to recognize the infection in tomato leaves with better accuracy and in less time.
Methods
Nowadays deep convolutional neural networks have attained surprising outcomes in several applications, together with the categorization of tomato leaves infected with several diseases. Our work is based on deep CNN with different residual networks. Finally; we have performed tomato leaves disease classification by using pre-trained deep CNN with the residual network using MATLAB available on the cloud.
Results
We have used a dataset of tomato leaves for the experiments which contain six different types of diseases with one healthy tomato leaf class. We have collected 6,594 tomato leaves dataset from Plant Village and we did not collect actual tomato leaves for testing. The outcome obtained by ResNet-50 shows a significant result with 96.35% accuracy for 50% training and 50% testing data and if we focus on time consumption for the outcome then ResNet-18 consumes 12.46 min for 70% training and 30% testing.
Conclusions
After observation of several outcomes, we have concluded that ResNet-50 shows a better accuracy for 50% training and 50% testing of data and ResNet-18 shows better efficiency for 70% training and 30% testing of data for the same dataset on the cloud.
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