Collection and Classification of Jasmine Rice Germination Using Convolutional Neural Networks

EasyChair Preprint 1238, version 2

Abstract

An assessment of rice-seeds germination is a process of measuring the quality of the seeds for the benefit of rice planting farms in Surin Province and the neighboring areas in Thailand. We need specialists or experts in the area of agriculture to evaluate the seeds germination by classifying them into normal and abnormal seeds which require a lot of times and hard work. In this paper, we present our dataset collection and use convolutional neural networks (CNNs) to classify normal and abnormal Jasmine rice-seeds after germination of 7 days. Our purpose is to use deep learning technique such as CNNs to do the work of evaluation or classification instead of specialists or experts. We collected 1,562 sample images of Jasmine rice seed germination and categorized them into two groups, normal and abnormal. We also collected an extra 76 images mixing abnormal and normal together for testing. We set 75% of our dataset as a training set and 25% as a validation set. We build CNNs of 6 hidden layers and in each layer consists of convolution-pooling-Relu modules. It is the binary network that results as 0 and 1 which represent normal and abnormal. Therefore, in the last layer, we use a sigmoid function to acquire our score. Our experimental results show that the effectiveness of using CNNs in our work is very high. We obtain an average accuracy of 99.57% and loss 0.01% on training and accuracy 96.43% and loss 0.48% on validation.

Keyphrases: Convolutional Neural Networks, Jasmine rice, Seeds germination, image classification, rice germination

@booklet{EasyChair:1238,
  author    = {Somsawut Nindam and Thai Leang Sung and Thong-Oon Manmai and Hyo Jong Lee},
  title     = {Collection and Classification of Jasmine Rice Germination Using Convolutional Neural Networks},
  howpublished = {EasyChair Preprint 1238},
  year      = {EasyChair, 2019}}