Identification of Crop Consuming Insect Pest from Visual Imagery Using Transfer Learning and Data Augmentation on Deep Neural Network

Identification and prevention of pest insects are essential requirements for proper crop cultivation. However, identifying pest insects can be a daunting and time consuming task because of the similarities of visual traits between different species. As a result, there are some necessities for a well performing automated system that can classify pest insects from image data. In this research, we propose a noble model that takes advantage of transfer learning and data augmentation to classify insect pest species from image data in the most accurate way. In the proposed model, three different Deep Neural Network (DNN) models were used for image classification: VGG19, Inception v3 and ResNet50. With appropriate data augmentation, Inception v3 achieved the best accuracy of 57.08% on a total of 102 insect species classification, beating the previous best result of 49.4% on the same dataset. Additionally, all the species were grouped based on the crops they consume. As Inception v3 was the best performing model across all the classes, it was also used to classify crop specific insect species. For eight different crops, an approximate range of 48.2% to 88.1% accuracy was achieved from classification. Finally, all the results were analyzed, compared and discussed.