Deep Transfer Learning based Acoustic Detection of Rice Weevils, Sitophilus Oryzae (L.) in Stored Grains - Rabano, Stephenn; K. Cabatuan, Melvin; Jose P. Dadios, Elmer; J. Calilung, Edwin; Rhay P. Vicerra, Ryan; Sybingco, Edwin; R. Regpala, Efren (International Journal of Engineering and Technology, 2018)
The presence of rice weevils is causing degradation of rice quantity and quality during storage. Classifying rice grade is critical since rice weevils are not easily detected. This study used deep transfer learning on spectrogram images of sounds to recognize the presence or absence of rice weevils in a sound clip. There are 1000 audio files with rice weevil presence and 1000 audio files with the absence of rice weevils in the dataset, each having a duration of 5 seconds. Random environments and random number and age of insects were considered to have models that are less dependent on the environment setting. The dataset was preprocessed to generate the spectrogram image of each audio clip. Features of those images were extracted to train some pre-trained Keras models on the dataset. In the dataset, 1400 images were used for training and 600 were used for testing. Each among the models Xception, ResNet50, InceptionResNetV2, and MobileNet obtained a rank-1 accuracy of 99.17% while VGG16, VGG19, and InceptionV3 all got a rank-1 accuracy of 99.33%. The average precision, average recall, and average F1 score in each trained model are all 99%. These account for the effectiveness of using deep transfer learning on spectrogram images of audio recordings in the detection of rice weevils in stored grains. This is also the first study that used deep transfer learning on spectrogram images in the acoustic detection of rice weevils. Â Â