Detection and Identification of Rice Pests Using Memory Efficient Convolutional Neural Network

Abstract

Rice pest detection is a very important part for the development of our agriculture. Numerous farmers are impacted worldwide by rice pests that frequently endanger the sustainability of rice production. There are many types of machine learning techniques for detecting the rice pests. CNNs (Convolutional Neural Networks) are currently regarded as the state-of-the-art technology for image recognition. Most of the models in existing researches worked with datasets that have small number of images and classes. In this paper, We have performed the training of our proposed model with 10400 images, containing ten different classes including Bacterial Leaf Blight, Bacterial Leaf Streak, Bacterial Panicle Blight, Blast, Brown Spot, Dead Heart, Downy Mildew, Healthy, Hispa and Tungro. A custom CNN has been used in the proposed model for pest detection, which will detect different classes of rice pests. To implement our model, we have used the Keras framework with a TensorFlow backend. In addition, our proposed model gives 88.18% validation accuracy while having only 0.57 million parameters.