ABSTRACT
Insect pest recognition and detection are vital for food security, a stable agricultural economy, and quality of life. To realize rapid detection and recognition of insect pests, methods inspired by the human visual systems were proposed in this chapter. Inspired by human visual attention, Saliency Using Natural statistics model (SUN) was used to generate saliency maps and detect region of interest (ROI) in a pest image. To extract the invariant features for representing the pest appearance, we extended the bio-inspired HMAX model in the following ways. Scale Invariant Feature Transform (SIFT) was integrated into the HMAX model to increase the invariance to rotational changes. Meanwhile, Non-negative Sparse Coding (NNSC) is used to simulate the simple cell responses. Moreover, invariant texture features were extracted based on Local Configuration Pattern (LCP) algorithm. Finally, the extracted features were fed to Support Vector Machines (SVM) for recognition. Experimental results demonstrated that the proposed method had an advantage over the compared methods: HMAX, Sparse Coding, and NIMBLE, and was comparable to the Deep Convolutional Network. The proposed method has achieved a good result with a recognition rate of 85.5% and could effectively recognize insect pests under complex environments. The proposed method has provided a new approach for insect pest detection and recognition.
