Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests - Nitin,; Gupta, Satinder Bal; Yadav, RajKumar; Bovand, Fatemeh; Tyagi, Pankaj Kumar (Frontiers in Plant Science, 2023)
Castor(Ricinus communis L.) is an important commercial crop produced castor oil that is considered an important primarily raw material for the manufacturing of medicines, lubricants, and others. However, the quality and quantity of castor turn out one of the important aspects, which can be degraded by various insect pests’ attacks. The traditional method to identify the correct category of pests required lots of time and mastery. To solve this issue, automatic insect pest detection methods with precision agriculture can help farmers to provide adequate support for sustainable agriculture development. For correct predictions, the recognition system demands’ a sufficient amount of data related to a real-world situation that cannot be captured always. In this, data augmentation is a popular technique applied for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. To solve the availability of a proper dataset for effective vision-based model training, a hybrid manipulation-based approach for data augmentation is proposed. The VGG16, VGG19, and ResNet50 deep convolutional neural networks are then adopted to analyze the impacts of the proposed augmentation method. The prediction results reveal that the proposed method tackles the challenges associated with adequate dataset size and improves overall performance significantly.