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Reseach Article
Tomato Leaf Disease Identification using Deep Reinforcement Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 28 |
| Year of Publication: 2022 |
| Authors: B.I. Madhubhashinie, W.H.C. Wickramaarachchi |
10.5120/ijca2022922358
|
B.I. Madhubhashinie, W.H.C. Wickramaarachchi . Tomato Leaf Disease Identification using Deep Reinforcement Learning. International Journal of Computer Applications. 184, 28 ( Sep 2022), 35-40. DOI=10.5120/ijca2022922358
Abstract
In the agriculture sector, tomato is a widely cultivated, most popular edible plant that contains rich nourishment and distinct flavor. Various factors, including bacteria, viruses, and fungus, are frequently responsible for tomato diseases. These diseases can be considered a prominent threat to cultivation. Therefore, the identification of leaf diseases plays a crucial role in taking disease control as well as increasing the quality and quantity of crop yield. With the idea of preserving harvest quality, the research aims to identify and categorize the diseases of tomato plant leaves. The initial focus of the research was to perform a comparative analysis between some existing Convolutional Neural Network (CNN) models to identify the best model for image recognition. The second phase of this research introduces a recurrent network to construct the model descriptions of Neural Networks (NN) and train this NN with Reinforcement Learning (RL) to optimize the anticipated accuracy of the constructed architectures on a dataset.
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