Detecting Pests and Diseases in Plants Using Efficient Network

Abstract

The agricultural sector in Indonesia is still faced with low agrarian production caused by pests and diseases. Therefore, agricultural land that is still vulnerable to pests but can detect the development of pest attacks must be designed. This study uses the PlantVillage dataset. The dataset will go through the preprocessing stage for dimension adjustment, and then the result will be used for building the network. The results are evaluated using a confusion matrix and showed that the convolutional neural network performs well in image processing and obtains architectural optimization in its field. The method we propose is an Efficient Network by selecting the correct input size. Implementing an Efficient Network in the convolutional neural network architecture increases its F1-score to 93%, indicating that Efficient Network has a higher F1-Score than the baseline convolution neural network. Implementing this network architecture can quickly increase the CNN baseline to a more varied target resource while maintaining the efficiency of the resulting model.