Crop pest recognition in natural scenes using convolutional neural networks

Crop diseases and insect pests are major agricultural problems worldwide, because the severity and extent of their occurrence causes significant crop losses. In addition, traditional crop pests recognition methods are limited, ineffective, and time-consuming due to the manual selection of the useful feature sets. This paper introduces a crop pest recognition method that accurately recognizes ten common species of crop pests by applying several deep convolutional neural networks (CNNs). The main contributions of this paper are (1) a manually collected and validated crop pest dataset is described and shared; (2) a fine-tuned GoogLeNet model is proposed to deal with the complicated backgrounds presented by farmland scenes, with pest classification results better than the original model; and (3) the fine-tuned GoogLeNet model obtains an improvement of 6.22% compared to the state-of-the-art method. As a result, the proposed model has the potential to be applied in real-world applications and further motivate research on crop disease identification.