Classification of five different rice seeds grown in Turkey with deep learning methods

Abstract

The increase in the world population and harmful environmental factors such as global warming necessitate a change in agricultural practices with the traditional method. Precision agriculture solutions offer many innovations to meet this increasing need. Using healthy, suitable and high-quality seeds is the first option that comes to mind in order to harvest more products from the fields. Seed classification is carried out in a labor-intensive manner. Due to the nature of this process, it is error-prone and also requires a high budget and time. The use of state-of-the-art methods such as Deep Learning in computer vision solutions enables the development of different applications in many areas. Rice is the most widely used grain worldwide after wheat and barley. This study aims to classify five different rice species grown in Turkey using four different Convolutional Neural Network (CNN) architectures. First, a new rice image dataset of five different species was created. Then, known and widely applied CNN architectures such as Visual Geometry Group (VGG), Residual Network (ResNet) and EfficientNets were trained and results were obtained. In addition, a new CNN architecture was designed and the results were compared with the other three architectures. The results showed that the VGG architecture generated the best accuracy value of 97%.

Keywords

• Convolutional neural networks
• VGG
• ResNet
• EfficientNet

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