Abstract
The major risk to food safety is Plant diseases and now its has become important obstacle to identity plant diseases in the starting phase itself to minimize the financial loss. To overcome this challenge a state of the art Convolution Neural Network is proposed to classify tomato leaf diseases with the help of computer vision and provide superb result. The transfer learning method is also used to make the model cost effective.
In this paper a transfer learning based CNN architecture is proposed to classify tomato leaf diseases. The dataset is combination of all the main public data available online like PlantVillage dataset, Plant Doc dataset and Mandely dataset which in total consist of 41863 images divided in 10 classes ranging from healthy to different types of tomato leaf diseases. The main focus is on ResNet50 network, a recognized CNN architecture which return the best accuracy of 99.2% and is extensively compared with existing work. The high accuracy makes the model practically useful that can be further extended to integrate with plant diseases classification system to operate in real world scenarios.
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Seth, V., Paulus, R., Kumar, M., Kumar, A. (2022). Tomato Leaf Diseases Detection. In: Mekhilef, S., Shaw, R.N., Siano, P. (eds) Innovations in Electrical and Electronic Engineering. ICEEE 2022. Lecture Notes in Electrical Engineering, vol 894. Springer, Singapore. https://doi.org/10.1007/978-981-19-1677-9_5
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